From c458939c8a4a21e5db0358f9866d2cfba7c948e3 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Sat, 29 Jun 2024 16:58:47 +0000 Subject: [PATCH] build based on 4afac10 --- dev/.documenter-siteinfo.json | 2 +- dev/api-callbacks.html | 6 +- dev/api-ctbase.html | 2 +- dev/api-description.html | 8 +-- dev/api-developers.html | 108 +++++++++++++++++----------------- dev/api-diffgeom.html | 46 +++++++-------- dev/api-exceptions.html | 2 +- dev/api-init.html | 2 +- dev/api-model.html | 70 +++++++++++----------- dev/api-parser.html | 10 ++-- dev/api-plot.html | 2 +- dev/api-print.html | 2 +- dev/api-repl.html | 4 +- dev/api-types.html | 32 +++++----- dev/api-utils.html | 20 +++---- dev/index.html | 2 +- 16 files changed, 159 insertions(+), 159 deletions(-) diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 95278399..eb5693ce 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.10.4","generation_timestamp":"2024-06-29T11:59:28","documenter_version":"1.5.0"}} \ No newline at end of file +{"documenter":{"julia_version":"1.10.4","generation_timestamp":"2024-06-29T16:58:42","documenter_version":"1.5.0"}} \ No newline at end of file diff --git a/dev/api-callbacks.html b/dev/api-callbacks.html index 7e31a283..0ab049b1 100644 --- a/dev/api-callbacks.html +++ b/dev/api-callbacks.html @@ -1,8 +1,8 @@ -Callbacks · CTBase.jl
GitHub

Callbacks

Index

Documentation

CTBase.get_priority_print_callbacks — Method
get_priority_print_callbacks(
+Callbacks · CTBase.jl
GitHub
Callbacks
Index
Documentation

+

Get the highest priority stop callbacks.

source
diff --git a/dev/api-ctbase.html b/dev/api-ctbase.html index bf138925..3369888f 100644 --- a/dev/api-ctbase.html +++ b/dev/api-ctbase.html @@ -1,2 +1,2 @@ -CTBase.jl · CTBase.jl
GitHub

CTBase.jl

Index

Documentation

CTBase.CTBase — Module

CTBase module.

Lists all the imported modules and packages:

  • Base
  • Core
  • DataStructures
  • DocStringExtensions
  • LinearAlgebra
  • MLStyle
  • Parameters
  • PrettyTables
  • Printf
  • ReplMaker
  • Unicode

List of all the exported names:

source
CTBase.TimesDisc — Type

Type alias for a grid of times. This is used to define a discretization of time interval given to solvers.

julia> const TimesDisc = Union{Times, StepRangeLen}

See also: Time, Times.

source
CTBase.Dimension — Type

Type alias for a dimension. This is used to define the dimension of the state space, the costate space, the control space, etc.

julia> const Dimension = Integer
source
+CTBase.jl · CTBase.jl
GitHub

CTBase.jl

Index

Documentation

CTBase.CTBase — Module

CTBase module.

Lists all the imported modules and packages:

  • Base
  • Core
  • DataStructures
  • DocStringExtensions
  • LinearAlgebra
  • MLStyle
  • Parameters
  • PrettyTables
  • Printf
  • ReplMaker
  • Unicode

List of all the exported names:

source
CTBase.TimesDisc — Type

Type alias for a grid of times. This is used to define a discretization of time interval given to solvers.

julia> const TimesDisc = Union{Times, StepRangeLen}

See also: Time, Times.

source
CTBase.Dimension — Type

Type alias for a dimension. This is used to define the dimension of the state space, the costate space, the control space, etc.

julia> const Dimension = Integer
source
diff --git a/dev/api-description.html b/dev/api-description.html index d9530eaa..2307ec15 100644 --- a/dev/api-description.html +++ b/dev/api-description.html @@ -1,7 +1,7 @@ Description · CTBase.jl
GitHub

Description

Index

Documentation

CTBase.Description — Type

A description is a tuple of symbols. Description is a type alias for a tuple of symbols.

julia> const Description = Tuple{DescVarArg}

See also: DescVarArg.

Example

Base.show is overloaded for descriptions, that is tuple of descriptions are printed as follows:

julia> display( ( (:a, :b), (:b, :c) ) )
 (:a, :b)
-(:b, :c)
source
CTBase.add — Method
add(
     x::Tuple{Vararg{Tuple{Vararg{Symbol}}}},
     y::Tuple{Vararg{Symbol}}
 ) -> Tuple{Tuple{Vararg{Symbol}}}
@@ -12,7 +12,7 @@
 (:a,)
 (:b,)
 julia> descriptions = add(descriptions, (:b,))
-ERROR: IncorrectArgument: the description (:b,) is already in ((:a,), (:b,))
source
CTBase.add — Method
add(
+ERROR: IncorrectArgument: the description (:b,) is already in ((:a,), (:b,))
source
CTBase.add — Method
add(
     x::Tuple{},
     y::Tuple{Vararg{Symbol}}
 ) -> Tuple{Tuple{Vararg{Symbol}}}
@@ -22,7 +22,7 @@
 julia> print(descriptions)
 ((:a,),)
 julia> descriptions[1]
-(:a,)
source
CTBase.getFullDescription — Method
getFullDescription(
     desc::Tuple{Vararg{Symbol}},
     desc_list::Tuple{Vararg{Tuple{Vararg{Symbol}}}}
 ) -> Tuple{Vararg{Symbol}}
@@ -31,4 +31,4 @@
 (:b, :c)
 (:a, :c)
 julia> getFullDescription((:a,), desc_list)
-(:a, :b)
source
+(:a, :b)source diff --git a/dev/api-developers.html b/dev/api-developers.html index 63f746fc..dffe57bc 100644 --- a/dev/api-developers.html +++ b/dev/api-developers.html @@ -1,91 +1,91 @@ -Developers · CTBase.jl
GitHub

Private functions

Index

Documentation

CTBase.DescVarArg — Constant

DescVarArg is a Vararg of symbols. DescVarArg is a type alias for a Vararg of symbols.

julia> const DescVarArg = Vararg{Symbol}

See also: Description.

source
Base.:\ — Method
\(
+Developers · CTBase.jl
GitHub
Private functions
Index
Documentation
CTBase.DescVarArg — Constant
DescVarArg is a Vararg of symbols. DescVarArg is a type alias for a Vararg of symbols.
julia> const DescVarArg = Vararg{Symbol}
See also: Description.
source
Base.:\ — Method
\(
     x::Tuple{Vararg{Symbol}},
     y::Tuple{Vararg{Symbol}}
 ) -> Tuple{Vararg{Symbol}}
 
Return the difference between the description x and the description y.
Example
julia> (:a, :b) \ (:a,)
-(:b,)
source
Base.show — Method
show(
     io::IO,
     _::MIME{Symbol("text/plain")},
     ocp::OptimalControlModel
 )
-
Print the optimal control problem.
source
Base.show — Method
show(
     io::IO,
     _::MIME{Symbol("text/plain")},
     sol::OptimalControlSolution
 )
-
Prints the solution.
source
Base.show — Method
show(
     io::IO,
     _::MIME{Symbol("text/plain")},
     descriptions::Tuple{Vararg{Tuple{Vararg{Symbol}}}}
 )
 
Print a tuple of descriptions.
Example
julia> display( ( (:a, :b), (:b, :c) ) )
 (:a, :b)
-(:b, :c)
source
CTBase.:⅋ — Method
⅋(
     X::VectorField{Autonomous, V<:VariableDependence},
     Y::VectorField{Autonomous, V<:VariableDependence}
 ) -> VectorField
 
"Directional derivative" of a vector field: internal and only used to compute efficiently the Lie bracket of two vector fields, autonomous case
Example
julia> X = VectorField(x -> [x[2], -x[1]])
 julia> Y = VectorField(x -> [x[1], x[2]])
 julia> CTBase.:(⅋)(X, Y)([1, 2])
-[2, -1]
source
CTBase.:⅋ — Method
⅋(
     X::VectorField{NonAutonomous, V<:VariableDependence},
     Y::VectorField{NonAutonomous, V<:VariableDependence}
 ) -> VectorField{NonAutonomous}
 
"Directional derivative" of a vector field: internal and only used to compute efficiently the Lie bracket of two vector fields, nonautonomous case
Example
julia> X = VectorField((t, x, v) -> [t + v[1] + v[2] + x[2], -x[1]], NonFixed, NonAutonomous)
 julia> Y = VectorField((t, x, v) ->  [v[1] + v[2] + x[1], x[2]], NonFixed, NonAutonomous)
 julia> CTBase.:(⅋)(X, Y)(1, [1, 2], [2, 3])
-[8, -1]
source
CTBase.__callbacks — Method
__callbacks() -> Tuple{}
-
Used to set the default value of the callbacks argument. The default value is (), which means that no additional callback is given.
source
CTBase.__callbacks — Method
__callbacks() -> Tuple{}
+
Used to set the default value of the callbacks argument. The default value is (), which means that no additional callback is given.
source
CTBase.__check_all_set — Method
__check_all_set(
     ocp::OptimalControlModel
 ) -> Union{Nothing, Bool}
-
Check if the parameters of an ocp are set.
source
CTBase.__check_control_set — Method
__check_control_set(ocp::OptimalControlModel) -> Bool
-
Throw UnauthorizedCall exception if the control of an ocp is not set.
source
CTBase.__check_control_set — Method
__check_control_set(ocp::OptimalControlModel) -> Bool
+
Throw UnauthorizedCall exception if the control of an ocp is not set.
source
CTBase.__check_dependencies — Method
__check_dependencies(
     dependencies::Tuple{Vararg{DataType}}
 ) -> Bool
-
Throw IncorrectArgument exception if dependencies arguments are incorrect.
source
CTBase.__check_is_time_set — Method
__check_is_time_set(ocp::OptimalControlModel) -> Bool
-
Throw UnauthorizedCall exception if the time of an ocp is not set.
source
CTBase.__check_state_set — Method
__check_state_set(ocp::OptimalControlModel) -> Bool
-
Throw UnauthorizedCall exception if the state of an ocp is not set.
source
CTBase.__check_is_time_set — Method
__check_is_time_set(ocp::OptimalControlModel) -> Bool
+
Throw UnauthorizedCall exception if the time of an ocp is not set.
source
CTBase.__check_state_set — Method
__check_state_set(ocp::OptimalControlModel) -> Bool
+
Throw UnauthorizedCall exception if the state of an ocp is not set.
source
CTBase.__check_variable_set — Method
__check_variable_set(
     ocp::OptimalControlModel{<:TimeDependence, NonFixed}
 ) -> Bool
-
Throw UnauthorizedCall exception if the variable of an ocp is not set.
source
CTBase.__constraint_label — Method
__constraint_label() -> Symbol
-
Used to set the default value of the label of a constraint. A unique value is given to each constraint using the gensym function and prefixing by :unamed.
source
CTBase.__control_components_names — Method
__control_components_names(m::Integer, name::String) -> Any
-
Used to set the default value of the names of the controls. The default value is ["u"] for a one dimensional control, and ["u₁", "u₂", ...] for a multi dimensional control.
source
CTBase.__control_name — Method
__control_name() -> String
-
Used to set the default value of the names of the control. The default value is "u".
source
CTBase.__criterion_type — Method
__criterion_type() -> Symbol
-
Used to set the default value of the type of criterion. Either :min or :max. The default value is :min. The other possible criterion type is :max.
source
CTBase.__display — Method
__display() -> Bool
-
Used to set the default value of the display argument. The default value is true, which means that the output is printed during resolution.
source
CTBase.__fun_time_dependence — Method
__fun_time_dependence() -> Type{Autonomous}
-
Used to set the default value of the time dependence of the functions.
The default value is Autonomous, which means that the functions are considered time independent. The other possible time dependence is NonAutonomous, which means that the functions are considered time dependent.
source
CTBase.__fun_variable_dependence — Method
__fun_variable_dependence() -> Type{Fixed}
-
Used to set the default value of the variable dependence of the functions.
The default value is Fixed, which means that the functions are considered variable independent. The other possible variable dependence is NonFixed, which means that the functions are considered variable dependent.
source
CTBase.__constraint_label — Method
__constraint_label() -> Symbol
+
Used to set the default value of the label of a constraint. A unique value is given to each constraint using the gensym function and prefixing by :unamed.
source
CTBase.__control_components_names — Method
__control_components_names(m::Integer, name::String) -> Any
+
Used to set the default value of the names of the controls. The default value is ["u"] for a one dimensional control, and ["u₁", "u₂", ...] for a multi dimensional control.
source
CTBase.__control_name — Method
__control_name() -> String
+
Used to set the default value of the names of the control. The default value is "u".
source
CTBase.__criterion_type — Method
__criterion_type() -> Symbol
+
Used to set the default value of the type of criterion. Either :min or :max. The default value is :min. The other possible criterion type is :max.
source
CTBase.__display — Method
__display() -> Bool
+
Used to set the default value of the display argument. The default value is true, which means that the output is printed during resolution.
source
CTBase.__fun_time_dependence — Method
__fun_time_dependence() -> Type{Autonomous}
+
Used to set the default value of the time dependence of the functions.
The default value is Autonomous, which means that the functions are considered time independent. The other possible time dependence is NonAutonomous, which means that the functions are considered time dependent.
source
CTBase.__fun_variable_dependence — Method
__fun_variable_dependence() -> Type{Fixed}
+
Used to set the default value of the variable dependence of the functions.
The default value is Fixed, which means that the functions are considered variable independent. The other possible variable dependence is NonFixed, which means that the functions are considered variable dependent.
source
CTBase.__get_AD_backend — Method
__get_AD_backend(
 
 ) -> ADTypes.AutoForwardDiff{nothing, Nothing}
-
Used to set the default value of Automatic Differentiation backend.
The default value is AutoForwardDiff(), that is the ForwardDiff package is used by default.
source
CTBase.__init_interpolation — Method
__init_interpolation() -> CTBase.var"#13#14"
-
Used to set the default interpolation function used for initialisation. The default value is Interpolations.linear_interpolation, which means that the initial guess is linearly interpolated.
source
CTBase.__mu_strategy_ipopt — Method
__mu_strategy_ipopt() -> String
-
Used to set the default value of the μ strategy of ipopt for the direct method. The default value is adaptive.
source
CTBase.__ocp_time_dependence — Method
__ocp_time_dependence() -> Type{Autonomous}
-
Used to set the default value of the time dependence of the Optimal Control Problem. The default value is Autonomous, which means that the Optimal Control Problem is considered time independent. The other possible time dependence is NonAutonomous, which means that all the functions used to define the  Optimal Control Problem are considered time dependent.
source
CTBase.__ocp_variable_dependence — Method
__ocp_variable_dependence() -> Type{Fixed}
-
Used to set the default value of the variable dependence of the Optimal Control Problem. The default value is Fixed, which means that the Optimal Control Problem is considered variable independent. The other possible variable dependence is NonFixed, which means that all the functions used to define the Optimal Control Problem are considered variable dependent.
source
CTBase.__print_level_ipopt — Method
__print_level_ipopt() -> Int64
-
Used to set the default value of the print level of ipopt for the direct method. The default value is 5.
source
CTBase.__state_components_names — Method
__state_components_names(n::Integer, name::String) -> Any
-
Used to set the default value of the names of the states. The default value is ["x"] for a one dimensional state, and ["x₁", "x₂", ...] for a multi dimensional state.
source
CTBase.__state_name — Method
__state_name() -> String
-
Used to set the default value of the name of the state. The default value is "x".
source
CTBase.__time_name — Method
__time_name() -> String
-
Used to set the default value of the name of the time. The default value is t.
source
CTBase.__variable_components_names — Method
__variable_components_names(q::Integer, name::String) -> Any
-
Used to set the default value of the names of the variables. The default value is ["v"] for a one dimensional variable, and ["v₁", "v₂", ...] for a multi dimensional variable.
source
CTBase.__variable_name — Method
__variable_name() -> String
-
Used to set the default value of the names of the variables. The default value is "v".
source
CTBase.expand — Method
expand(x::Matrix{<:Real}) -> Vector{<:Real}
-
Return expand(matrix2vec(x, 1))
source
CTBase.expand — Method
expand(x::Vector{<:Vector{<:Real}}) -> Vector{<:Real}
-
Equivalent to vec2vec(x)
source
CTBase.expr_it — Method
expr_it(e, _Expr, f) -> Any
-
Expr iterator: apply _Expr to nodes and f to leaves of the AST.
Example
julia> id(e) = expr_it(e, Expr, x -> x)
source
CTBase.has — Method
has(e, x, t) -> Union{Missing, Bool}
+
Used to set the default value of Automatic Differentiation backend.
The default value is AutoForwardDiff(), that is the ForwardDiff package is used by default.
source
CTBase.__init_interpolation — Method
__init_interpolation() -> CTBase.var"#13#14"
+
Used to set the default interpolation function used for initialisation. The default value is Interpolations.linear_interpolation, which means that the initial guess is linearly interpolated.
source
CTBase.__mu_strategy_ipopt — Method
__mu_strategy_ipopt() -> String
+
Used to set the default value of the μ strategy of ipopt for the direct method. The default value is adaptive.
source
CTBase.__ocp_time_dependence — Method
__ocp_time_dependence() -> Type{Autonomous}
+
Used to set the default value of the time dependence of the Optimal Control Problem. The default value is Autonomous, which means that the Optimal Control Problem is considered time independent. The other possible time dependence is NonAutonomous, which means that all the functions used to define the  Optimal Control Problem are considered time dependent.
source
CTBase.__ocp_variable_dependence — Method
__ocp_variable_dependence() -> Type{Fixed}
+
Used to set the default value of the variable dependence of the Optimal Control Problem. The default value is Fixed, which means that the Optimal Control Problem is considered variable independent. The other possible variable dependence is NonFixed, which means that all the functions used to define the Optimal Control Problem are considered variable dependent.
source
CTBase.__print_level_ipopt — Method
__print_level_ipopt() -> Int64
+
Used to set the default value of the print level of ipopt for the direct method. The default value is 5.
source
CTBase.__state_components_names — Method
__state_components_names(n::Integer, name::String) -> Any
+
Used to set the default value of the names of the states. The default value is ["x"] for a one dimensional state, and ["x₁", "x₂", ...] for a multi dimensional state.
source
CTBase.__state_name — Method
__state_name() -> String
+
Used to set the default value of the name of the state. The default value is "x".
source
CTBase.__time_name — Method
__time_name() -> String
+
Used to set the default value of the name of the time. The default value is t.
source
CTBase.__variable_components_names — Method
__variable_components_names(q::Integer, name::String) -> Any
+
Used to set the default value of the names of the variables. The default value is ["v"] for a one dimensional variable, and ["v₁", "v₂", ...] for a multi dimensional variable.
source
CTBase.__variable_name — Method
__variable_name() -> String
+
Used to set the default value of the names of the variables. The default value is "v".
source
CTBase.expand — Method
expand(x::Matrix{<:Real}) -> Vector{<:Real}
+
Return expand(matrix2vec(x, 1))
source
CTBase.expand — Method
expand(x::Vector{<:Vector{<:Real}}) -> Vector{<:Real}
+
Equivalent to vec2vec(x)
source
CTBase.expr_it — Method
expr_it(e, _Expr, f) -> Any
+
Expr iterator: apply _Expr to nodes and f to leaves of the AST.
Example
julia> id(e) = expr_it(e, Expr, x -> x)
source
CTBase.has — Method
has(e, x, t) -> Union{Missing, Bool}
 
Return true if e contains a (...x...)(t) call.
Example
julia> e = :( ∫( x[1](t)^2 + 2*u(t) ) → min )
 :(∫((x[1])(t) ^ 2 + 2 * u(t)) → min)
 
@@ -93,7 +93,7 @@
 true
 
 julia> has(e, :u, :t)
-true
source
CTBase.has — Method
has(e, e1) -> Union{Missing, Bool}
 
Return true if e contains e1.
Example
julia> e = :( ∫( x[1](t)^2 + 2*u(t) ) → min )
 :(∫((x[1])(t) ^ 2 + 2 * u(t)) → min)
 
@@ -125,13 +125,13 @@
 true
 
 julia> !has(:x, :y)
-true
source
CTBase.matrix2vec — Function
matrix2vec(x::Matrix{<:Real}) -> Vector{<:Vector{<:Real}}
 matrix2vec(
     x::Matrix{<:Real},
     dim::Integer
 ) -> Vector{<:Vector{<:Real}}
-
Transforms x to a Vector{<:Vector{<:ctNumber}}.
Note. dim ∈ {1, 2} is the dimension along which the matrix is transformed.
source
CTBase.parse! — Method
parse!(p, ocp, e; log) -> Union{Expr, LineNumberNode}
-
Foo
Example
Foo
source
CTBase.subs — Method
subs(e, e1::Union{Real, Symbol}, e2) -> Any
+
Transforms x to a Vector{<:Vector{<:ctNumber}}.
Note. dim ∈ {1, 2} is the dimension along which the matrix is transformed.
source
CTBase.parse! — Method
parse!(p, ocp, e; log) -> Union{Expr, LineNumberNode}
+
Foo
Example
Foo
source
CTBase.subs — Method
subs(e, e1::Union{Real, Symbol}, e2) -> Any
 
Substitute expression e1 by expression e2 in expression e.
Examples
julia> e = :( ∫( r(t)^2 + 2u₁(t)) → min )
 :(∫(r(t) ^ 2 + 2 * u₁(t)) → min)
 
@@ -152,9 +152,9 @@
 :((x[1])(0) * (2 * x(tf)) - (x[2])(tf) * (2 * x(0)))
 
 julia> x0 = Symbol(x, 0); subs(e, :( $x[1]($(t0)) ), :( $x0[1] ))
-:(x0[1] * (2 * x(tf)) - (x[2])(tf) * (2 * x(0)))
source
CTBase.vec2vec — Method
vec2vec(
     x::Vector{<:Real},
     n::Integer
 ) -> Vector{<:Vector{<:Real}}
-
Transforms x to a Vector{<:Vector{<:ctNumber}}.
source
CTBase.vec2vec — Method
vec2vec(x::Vector{<:Vector{<:Real}}) -> Vector{<:Real}
-
Transforms x to a Vector{<:ctNumber}.
source

+

Transforms x to a Vector{<:Vector{<:ctNumber}}.

source
CTBase.vec2vec — Method
vec2vec(x::Vector{<:Vector{<:Real}}) -> Vector{<:Real}
+

Transforms x to a Vector{<:ctNumber}.

source
diff --git a/dev/api-diffgeom.html b/dev/api-diffgeom.html index 7f4dbbe0..cd53b885 100644 --- a/dev/api-diffgeom.html +++ b/dev/api-diffgeom.html @@ -1,5 +1,5 @@ -Differential geometry · CTBase.jl
GitHub

Differential geometry

Index

Documentation

CTBase.:⋅ — Method
⋅(X::Function, f::Function) -> Function
+Differential geometry · CTBase.jl
GitHub
Differential geometry
Index
Documentation
CTBase.:⋅ — Method
⋅(X::Function, f::Function) -> Function
 
Lie derivative of a scalar function along a function. In this case both functions will be considered autonomous and non-variable.
Example
julia> φ = x -> [x[2], -x[1]]
 julia> f = x -> x[1]^2 + x[2]^2
 julia> (φ⋅f)([1, 2])
@@ -7,7 +7,7 @@
 julia> φ = (t, x, v) -> [t + x[2] + v[1], -x[1] + v[2]]
 julia> f = (t, x, v) -> t + x[1]^2 + x[2]^2
 julia> (φ⋅f)(1, [1, 2], [2, 1])
-MethodError
source
CTBase.:⋅ — Method
⋅(
     X::VectorField{Autonomous, <:VariableDependence},
     f::Function
 ) -> CTBase.var"#97#99"{VectorField{Autonomous, var"#s98"}, <:Function} where var"#s98"<:VariableDependence
@@ -15,7 +15,7 @@
 julia> X = VectorField(φ)
 julia> f = x -> x[1]^2 + x[2]^2
 julia> (X⋅f)([1, 2])
-0
source
CTBase.:⋅ — Method
⋅(
     X::VectorField{NonAutonomous, <:VariableDependence},
     f::Function
 ) -> CTBase.var"#101#103"{VectorField{NonAutonomous, var"#s98"}, <:Function} where var"#s98"<:VariableDependence
@@ -23,7 +23,7 @@
 julia> X = VectorField(φ, NonAutonomous, NonFixed)
 julia> f = (t, x, v) -> t + x[1]^2 + x[2]^2
 julia> (X⋅f)(1, [1, 2], [2, 1])
-10
source
CTBase.Lie — Method
Lie(
     X::Function,
     f::Function,
     dependences::DataType...
@@ -35,7 +35,7 @@
 julia> φ = (t, x, v) -> [t + x[2] + v[1], -x[1] + v[2]]
 julia> f = (t, x, v) -> t + x[1]^2 + x[2]^2
 julia> Lie(φ, f, NonAutonomous, NonFixed)(1, [1, 2], [2, 1])
-10
source
CTBase.Lie — Method
Lie(
     X::Function,
     f::Function;
     autonomous,
@@ -48,7 +48,7 @@
 julia> φ = (t, x, v) -> [t + x[2] + v[1], -x[1] + v[2]]
 julia> f = (t, x, v) -> t + x[1]^2 + x[2]^2
 julia> Lie(φ, f, autonomous=false, variable=true)(1, [1, 2], [2, 1])
-10
source
CTBase.Lie — Method
Lie(X::VectorField, f::Function) -> Function
 
Lie derivative of a scalar function along a vector field.
Example
julia> φ = x -> [x[2], -x[1]]
 julia> X = VectorField(φ)
 julia> f = x -> x[1]^2 + x[2]^2
@@ -58,7 +58,7 @@
 julia> X = VectorField(φ, NonAutonomous, NonFixed)
 julia> f = (t, x, v) -> t + x[1]^2 + x[2]^2
 julia> Lie(X, f)(1, [1, 2], [2, 1])
-10
source
CTBase.Lie — Method
Lie(
     X::VectorField{Autonomous, V<:VariableDependence},
     Y::VectorField{Autonomous, V<:VariableDependence}
 ) -> VectorField
@@ -67,7 +67,7 @@
 julia> X = VectorField(f)
 julia> Y = VectorField(g)
 julia> Lie(X, Y)([1, 2])
-[7, -14]
source
CTBase.Lie — Method
Lie(
     X::VectorField{NonAutonomous, V<:VariableDependence},
     Y::VectorField{NonAutonomous, V<:VariableDependence}
 ) -> VectorField{NonAutonomous}
@@ -76,7 +76,7 @@
 julia> X = VectorField(f, NonAutonomous, NonFixed)
 julia> Y = VectorField(g, NonAutonomous, NonFixed)
 julia> Lie(X, Y)(1, [1, 2], 1)
-[-7,12]
source
CTBase.Lift — Method
Lift(
     X::Function,
     dependences::DataType...
 ) -> HamiltonianLift
@@ -85,13 +85,13 @@
 2
 julia> H = Lift((t, x, v) -> 2x + t - v, NonAutonomous, NonFixed)
 julia> H(1, 1, 1, 1)
-2
source
CTBase.Lift — Method
Lift(X::Function; autonomous, variable) -> HamiltonianLift
+2
source
CTBase.Lift — Method
Lift(X::Function; autonomous, variable) -> HamiltonianLift
 
Return the HamiltonianLift of a function. Dependencies are specified with boolean : autonomous and variable.
Example
julia> H = Lift(x -> 2x)
 julia> H(1, 1)
 2
 julia> H = Lift((t, x, v) -> 2x + t - v, autonomous=false, variable=true)
 julia> H(1, 1, 1, 1)
-2
source
CTBase.Lift — Method
Lift(X::VectorField) -> HamiltonianLift
 
Return the HamiltonianLift of a VectorField.
Example
julia> HL = Lift(VectorField(x -> [x[1]^2,x[2]^2], autonomous=true, variable=false))
 julia> HL([1, 0], [0, 1])
 0
@@ -106,7 +106,7 @@
 2
 julia> H = Lift((t, x, v) -> 2x + t - v, NonAutonomous, NonFixed)
 julia> H(1, 1, 1, 1)
-2
source
CTBase.Poisson — Method
Poisson(
     f::Function,
     g::Function,
     dependences::DataType...
@@ -118,7 +118,7 @@
 julia> f = (t, x, p, v) -> t*v[1]*x[2]^2 + 2x[1]^2 + p[1]^2 + v[2]
 julia> g = (t, x, p, v) -> 3x[2]^2 + -x[1]^2 + p[2]^2 + p[1] + t - v[2]
 julia> Poisson(f, g, NonAutonomous, NonFixed)(2, [1, 2], [2, 1], [4, 4])
--76
source
CTBase.Poisson — Method
Poisson(
     f::Function,
     g::Function;
     autonomous,
@@ -131,7 +131,7 @@
 julia> f = (t, x, p, v) -> t*v[1]*x[2]^2 + 2x[1]^2 + p[1]^2 + v[2]
 julia> g = (t, x, p, v) -> 3x[2]^2 + -x[1]^2 + p[2]^2 + p[1] + t - v[2]
 julia> Poisson(f, g, autonomous=false, variable=true)(2, [1, 2], [2, 1], [4, 4])
--76
source
CTBase.Poisson — Method
Poisson(
     f::AbstractHamiltonian{Autonomous, V<:VariableDependence},
     g::AbstractHamiltonian{Autonomous, V<:VariableDependence}
 ) -> HamiltonianLift
@@ -144,7 +144,7 @@
 julia> Poisson(f, G)([1, 2], [2, 1])
 -20
 julia> Poisson(F, g)([1, 2], [2, 1])
--20
source
CTBase.Poisson — Method
Poisson(
     f::AbstractHamiltonian{NonAutonomous, V<:VariableDependence},
     g::AbstractHamiltonian{NonAutonomous, V<:VariableDependence}
 ) -> HamiltonianLift
@@ -155,7 +155,7 @@
 julia> Poisson(F, G)(2, [1, 2], [2, 1], [4, 4])
 -76
 julia> Poisson(f, g, NonAutonomous, NonFixed)(2, [1, 2], [2, 1], [4, 4])
--76
source
CTBase.Poisson — Method
Poisson(
     f::AbstractHamiltonian{T<:TimeDependence, V<:VariableDependence},
     g::Function
 ) -> Hamiltonian
@@ -168,7 +168,7 @@
 julia> g = (t, x, p, v) -> 3x[2]^2 + -x[1]^2 + p[2]^2 + p[1] + t - v[2]
 julia> F = Hamiltonian(f, autonomous=false, variable=true)
 julia> Poisson(F, g)(2, [1, 2], [2, 1], [4, 4])
--76
source
CTBase.Poisson — Method
Poisson(
     f::Function,
     g::AbstractHamiltonian{T<:TimeDependence, V<:VariableDependence}
 ) -> Hamiltonian
@@ -181,7 +181,7 @@
 julia> g = (t, x, p, v) -> 3x[2]^2 + -x[1]^2 + p[2]^2 + p[1] + t - v[2]
 julia> G = Hamiltonian(g, autonomous=false, variable=true)
 julia> Poisson(f, G)(2, [1, 2], [2, 1], [4, 4])
--76
source
CTBase.Poisson — Method
Poisson(
     f::HamiltonianLift{T<:TimeDependence, V<:VariableDependence},
     g::HamiltonianLift{T<:TimeDependence, V<:VariableDependence}
 ) -> HamiltonianLift
@@ -196,9 +196,9 @@
 julia> F = Lift(f, NonAutonomous, NonFixed)
 julia> G = Lift(g, NonAutonomous, NonFixed)
 julia> Poisson(F, G)(2, [1, 2], [2, 1], [4, 4])
-100
source
CTBase.∂ₜ — Method
∂ₜ(f) -> CTBase.var"#106#108"
 
Partial derivative wrt time of a function.
Example
julia> ∂ₜ((t,x) -> t*x)(0,8)
-8
source
CTBase.@Lie — Macro
Macros for Poisson brackets
Example
julia> H0 = (x, p) -> 0.5*(x[1]^2+x[2]^2+p[1]^2)
+8
source
CTBase.@Lie — Macro
Macros for Poisson brackets
Example
julia> H0 = (x, p) -> 0.5*(x[1]^2+x[2]^2+p[1]^2)
 julia> H1 = (x, p) -> 0.5*(x[1]^2+x[2]^2+p[2]^2)
 julia> @Lie {H0, H1}([1, 2, 3], [1, 0, 7]) autonomous=true variable=false
 #
@@ -212,14 +212,14 @@
 #
 julia> H0 = (t, x, p, v) -> 0.5*(x[1]^2+x[2]^2+p[1]^2+v)
 julia> H1 = (t, x, p, v) -> 0.5*(x[1]^2+x[2]^2+p[2]^2+v)
-julia> @Lie {H0, H1}(1, [1, 2, 3], [1, 0, 7], 2) autonomous=false variable=true
source
CTBase.@Lie — Macro
Macros for Lie and Poisson brackets
Example
julia> H0 = (t, x, p) -> 0.5*(x[1]^2+x[2]^2+p[1]^2)
+julia> @Lie {H0, H1}(1, [1, 2, 3], [1, 0, 7], 2) autonomous=false variable=true
source
CTBase.@Lie — Macro
Macros for Lie and Poisson brackets
Example
julia> H0 = (t, x, p) -> 0.5*(x[1]^2+x[2]^2+p[1]^2)
 julia> H1 = (t, x, p) -> 0.5*(x[1]^2+x[2]^2+p[2]^2)
 julia> @Lie {H0, H1}(1, [1, 2, 3], [1, 0, 7]) autonomous=false
 #
 julia> H0 = (x, p, v) -> 0.5*(x[1]^2+x[2]^2+p[1]^2+v)
 julia> H1 = (x, p, v) -> 0.5*(x[1]^2+x[2]^2+p[2]^2+v)
 julia> @Lie {H0, H1}([1, 2, 3], [1, 0, 7], 2) variable=true
-#
source
CTBase.@Lie — Macro
Macros for Lie and Poisson brackets
Example
julia> F0 = VectorField(x -> [x[1], x[2], (1-x[3])])
+#
source
CTBase.@Lie — Macro
Macros for Lie and Poisson brackets
Example
julia> F0 = VectorField(x -> [x[1], x[2], (1-x[3])])
 julia> F1 = VectorField(x -> [0, -x[3], x[2]])
 julia> @Lie [F0, F1]([1, 2, 3])
 [0, 5, 4]
@@ -252,4 +252,4 @@
 julia> H0 = Hamiltonian((t, x, p, v) -> 0.5*(x[1]^2+x[2]^2+p[1]^2+v), autonomous=false, variable=true)
 julia> H1 = Hamiltonian((t, x, p, v) -> 0.5*(x[1]^2+x[2]^2+p[2]^2+v), autonomous=false, variable=true)
 julia> @Lie {H0, H1}(1, [1, 2, 3], [1, 0, 7], 2)
-#
source

+#
source
diff --git a/dev/api-exceptions.html b/dev/api-exceptions.html index f5364456..7a277f08 100644 --- a/dev/api-exceptions.html +++ b/dev/api-exceptions.html @@ -1,2 +1,2 @@ -Exceptions · CTBase.jl
GitHub

Exceptions

Index

Documentation

CTBase.AmbiguousDescription — Type
struct AmbiguousDescription <: CTException

Exception thrown when the description is ambiguous / incorrect.

Fields

  • var::Tuple{Vararg{Symbol}}
source
CTBase.NotImplemented — Type
struct NotImplemented <: CTException

Exception thrown when a method is not implemented.

Fields

  • var::String
source
CTBase.ParsingError — Type
struct ParsingError <: CTException

Exception thrown for syntax error during abstract parsing.

Fields

  • var::String
source
CTBase.UnauthorizedCall — Type
struct UnauthorizedCall <: CTException

Exception thrown when a call to a function is not authorized.

Fields

  • var::String
source
+Exceptions · CTBase.jl
GitHub

Exceptions

Index

Documentation

CTBase.AmbiguousDescription — Type
struct AmbiguousDescription <: CTException

Exception thrown when the description is ambiguous / incorrect.

Fields

  • var::Tuple{Vararg{Symbol}}
source
CTBase.NotImplemented — Type
struct NotImplemented <: CTException

Exception thrown when a method is not implemented.

Fields

  • var::String
source
CTBase.ParsingError — Type
struct ParsingError <: CTException

Exception thrown for syntax error during abstract parsing.

Fields

  • var::String
source
CTBase.UnauthorizedCall — Type
struct UnauthorizedCall <: CTException

Exception thrown when a call to a function is not authorized.

Fields

  • var::String
source
diff --git a/dev/api-init.html b/dev/api-init.html index e188924f..4abbd4dd 100644 --- a/dev/api-init.html +++ b/dev/api-init.html @@ -3,4 +3,4 @@ julia> init = OptimalControlInit(x_init=[0.1, 0.2], u_init=0.3) julia> init = OptimalControlInit(x_init=[0.1, 0.2], u_init=0.3, v_init=0.5) julia> init = OptimalControlInit(x_init=[0.1, 0.2], u_init=t->sin(t), v_init=0.5) -julia> init = OptimalControlInit(sol)source +julia> init = OptimalControlInit(sol)source diff --git a/dev/api-model.html b/dev/api-model.html index 920d8b79..70c6f5d0 100644 --- a/dev/api-model.html +++ b/dev/api-model.html @@ -4,14 +4,14 @@ ) -> OptimalControlModel{Autonomous, Fixed}

Return a new OptimalControlModel instance, that is a model of an optimal control problem.

The model is defined by the following argument:

Examples

julia> ocp = Model()
 julia> ocp = Model(NonAutonomous)
-julia> ocp = Model(Autonomous, NonFixed)
Note
  • If the time dependence of the model is defined as nonautonomous, then, the dynamics function, the lagrange cost and the path constraints must be defined as functions of time and state, and possibly control. If the model is defined as autonomous, then, the dynamics function, the lagrange cost and the path constraints must be defined as functions of state, and possibly control.
source
CTBase.Model — Method
Model(
+julia> ocp = Model(Autonomous, NonFixed)
Note
  • If the time dependence of the model is defined as nonautonomous, then, the dynamics function, the lagrange cost and the path constraints must be defined as functions of time and state, and possibly control. If the model is defined as autonomous, then, the dynamics function, the lagrange cost and the path constraints must be defined as functions of state, and possibly control.
source
CTBase.Model — Method
Model(
 ;
     autonomous,
     variable
 ) -> OptimalControlModel{Autonomous, Fixed}

Return a new OptimalControlModel instance, that is a model of an optimal control problem.

The model is defined by the following optional keyword argument:

  • autonomous: either true or false. Default is true.
  • variable: either true or false. Default is false.

Examples

julia> ocp = Model()
 julia> ocp = Model(autonomous=false)
-julia> ocp = Model(autonomous=false, variable=true)
Note
  • If the time dependence of the model is defined as nonautonomous, then, the dynamics function, the lagrange cost and the path constraints must be defined as functions of time and state, and possibly control. If the model is defined as autonomous, then, the dynamics function, the lagrange cost and the path constraints must be defined as functions of state, and possibly control.
source
CTBase.constraint! — Method
constraint!(
+julia> ocp = Model(autonomous=false, variable=true)
Note
  • If the time dependence of the model is defined as nonautonomous, then, the dynamics function, the lagrange cost and the path constraints must be defined as functions of time and state, and possibly control. If the model is defined as autonomous, then, the dynamics function, the lagrange cost and the path constraints must be defined as functions of state, and possibly control.
source
CTBase.constraint! — Method
constraint!(
     ocp::OptimalControlModel{T<:TimeDependence, V<:VariableDependence},
     type::Symbol;
     rg,
@@ -55,17 +55,17 @@
 # time dependent and variable dependent ocp
 julia> constraint!(ocp, :control; f = (t, u, v) -> 2u+v[2], lb=0, ub=1)
 julia> constraint!(ocp, :state; f = (t, x, v) -> x-t*v[1], lb=[ 0, 0, 0 ], ub=[ 1, 2, 1 ])
-julia> constraint!(ocp, :mixed; f = (t, x, u, v) -> x[1]*v[2]-u, lb=0, ub=1)
source
CTBase.constraint — Method
constraint(
+julia> constraint!(ocp, :mixed; f = (t, x, u, v) -> x[1]*v[2]-u, lb=0, ub=1)
source
CTBase.constraint — Method
constraint(
     ocp::OptimalControlModel{T<:TimeDependence, V<:VariableDependence},
     label::Symbol
 ) -> Any

Retrieve a labeled constraint. The result is a function associated with the constraint computation (not taking into account provided value / bounds).

Example

julia> constraint!(ocp, :initial, 0, :c0)
 julia> c = constraint(ocp, :c0)
 julia> c(1)
-1
source
CTBase.constraints_labels — Method
constraints_labels(
+1
source
CTBase.constraints_labels — Method
constraints_labels(
     ocp::OptimalControlModel
 ) -> Base.KeySet{Symbol, Dict{Symbol, Tuple}}
-

Return the labels of the constraints as a Base.keys.

Example

julia> constraints_labels(ocp)
source
CTBase.control! — Function
control!(ocp::OptimalControlModel, m::Integer)
+

Return the labels of the constraints as a Base.keys.

Example

julia> constraints_labels(ocp)
source
CTBase.control! — Function
control!(ocp::OptimalControlModel, m::Integer)
 control!(ocp::OptimalControlModel, m::Integer, name::String)
 control!(
     ocp::OptimalControlModel,
@@ -101,54 +101,54 @@
 julia> ocp.control_dimension
 2
 julia> ocp.control_components_names
-["v₁", "v₂"]
source
CTBase.dim_boundary_constraints — Method
dim_boundary_constraints(
+["v₁", "v₂"]
source
CTBase.dim_boundary_constraints — Method
dim_boundary_constraints(
     ocp::OptimalControlModel
 ) -> Union{Nothing, Integer}
-

Return the dimension of the boundary constraints (nothing if not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dim_control_constraints — Method
dim_control_constraints(
+

Return the dimension of the boundary constraints (nothing if not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dim_control_constraints — Method
dim_control_constraints(
     ocp::OptimalControlModel
 ) -> Union{Nothing, Integer}
-

Return the dimension of nonlinear control constraints (nothing if not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dim_control_range — Method
dim_control_range(
+

Return the dimension of nonlinear control constraints (nothing if not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dim_control_range — Method
dim_control_range(
     ocp::OptimalControlModel
 ) -> Union{Nothing, Integer}
-

Return the dimension of range constraints on control (nothing if not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dim_mixed_constraints — Method
dim_mixed_constraints(
+

Return the dimension of range constraints on control (nothing if not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dim_mixed_constraints — Method
dim_mixed_constraints(
     ocp::OptimalControlModel
 ) -> Union{Nothing, Integer}
-

Return the dimension of nonlinear mixed constraints (nothing if not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dim_path_constraints — Method
dim_path_constraints(ocp::OptimalControlModel) -> Any
-

Return the dimension of nonlinear path (state + control + mixed) constraints (nothing if one of them is not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dim_state_constraints — Method
dim_state_constraints(
+

Return the dimension of nonlinear mixed constraints (nothing if not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dim_path_constraints — Method
dim_path_constraints(ocp::OptimalControlModel) -> Any
+

Return the dimension of nonlinear path (state + control + mixed) constraints (nothing if one of them is not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dim_state_constraints — Method
dim_state_constraints(
     ocp::OptimalControlModel
 ) -> Union{Nothing, Integer}
-

Return the dimension of nonlinear state constraints (nothing if not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dim_state_range — Method
dim_state_range(
+

Return the dimension of nonlinear state constraints (nothing if not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dim_state_range — Method
dim_state_range(
     ocp::OptimalControlModel
 ) -> Union{Nothing, Integer}
-

Return the dimension of range constraints on state (nothing if not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dim_variable_constraints — Method
dim_variable_constraints(
+

Return the dimension of range constraints on state (nothing if not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dim_variable_constraints — Method
dim_variable_constraints(
     ocp::OptimalControlModel
 ) -> Union{Nothing, Integer}
-

Return the dimension of nonlinear variable constraints (nothing if not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dim_variable_range — Method
dim_variable_range(
+

Return the dimension of nonlinear variable constraints (nothing if not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dim_variable_range — Method
dim_variable_range(
     ocp::OptimalControlModel
 ) -> Union{Nothing, Integer}
-

Return the dimension of range constraints on variable (nothing if not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dynamics! — Method
dynamics!(
+

Return the dimension of range constraints on variable (nothing if not knonw). Information is updated after nlp_constraints! is called.

source
CTBase.dynamics! — Method
dynamics!(
     ocp::OptimalControlModel{T<:TimeDependence, V<:VariableDependence},
     f::Function
 )
-

Set the dynamics.

Note

You can use dynamics! only once to define the dynamics.

  • The state, control and variable dimensions must be set before. Use state!, control! and variable!.
  • The times must be set before. Use time!.
  • When an element is of dimension 1, consider it as a scalar.

Example

julia> dynamics!(ocp, f)
source
CTBase.has_free_final_time — Method
has_free_final_time(ocp::OptimalControlModel) -> Bool
-

Return true if the model has been defined with free final time.

source
CTBase.has_free_initial_time — Method
has_free_initial_time(ocp::OptimalControlModel) -> Bool
-

Return true if the model has been defined with free initial time.

source
CTBase.has_lagrange_cost — Method
has_lagrange_cost(ocp::OptimalControlModel) -> Bool
-

Return true if the model has been defined with lagrange cost.

source
CTBase.has_mayer_cost — Method
has_mayer_cost(ocp::OptimalControlModel) -> Bool
-

Return true if the model has been defined with mayer cost.

source
CTBase.is_autonomous — Method
is_autonomous(ocp::OptimalControlModel{Autonomous}) -> Bool
-

Return true if the model is autonomous.

source
CTBase.is_fixed — Method
is_fixed(
+

Set the dynamics.

Note

You can use dynamics! only once to define the dynamics.

  • The state, control and variable dimensions must be set before. Use state!, control! and variable!.
  • The times must be set before. Use time!.
  • When an element is of dimension 1, consider it as a scalar.

Example

julia> dynamics!(ocp, f)
source
CTBase.has_free_final_time — Method
has_free_final_time(ocp::OptimalControlModel) -> Bool
+

Return true if the model has been defined with free final time.

source
CTBase.has_free_initial_time — Method
has_free_initial_time(ocp::OptimalControlModel) -> Bool
+

Return true if the model has been defined with free initial time.

source
CTBase.has_lagrange_cost — Method
has_lagrange_cost(ocp::OptimalControlModel) -> Bool
+

Return true if the model has been defined with lagrange cost.

source
CTBase.has_mayer_cost — Method
has_mayer_cost(ocp::OptimalControlModel) -> Bool
+

Return true if the model has been defined with mayer cost.

source
CTBase.is_autonomous — Method
is_autonomous(ocp::OptimalControlModel{Autonomous}) -> Bool
+

Return true if the model is autonomous.

source
CTBase.is_fixed — Method
is_fixed(
     ocp::OptimalControlModel{<:TimeDependence, Fixed}
 ) -> Bool
-

Return true if the model is fixed (= has no variable).

source
CTBase.is_max — Method
is_max(ocp::OptimalControlModel) -> Bool
-

Return true if the criterion type of ocp is :max.

source
CTBase.is_min — Method
is_min(ocp::OptimalControlModel) -> Bool
-

Return true if the criterion type of ocp is :min.

source
CTBase.is_time_dependent — Method
is_time_dependent(ocp::OptimalControlModel) -> Bool
-

Return true if the model has been defined as time dependent.

source
CTBase.is_time_independent — Method
is_time_independent(ocp::OptimalControlModel) -> Bool
-

Return true if the model has been defined as time independent.

source
CTBase.is_variable_dependent — Method
is_variable_dependent(ocp::OptimalControlModel) -> Bool
-

Return true if the model has been defined as variable dependent.

source
CTBase.is_variable_independent — Method
is_variable_independent(ocp::OptimalControlModel) -> Bool
-

Return true if the model has been defined as variable independent.

source
CTBase.nlp_constraints! — Method
nlp_constraints!(
+

Return true if the model is fixed (= has no variable).

source
CTBase.is_max — Method
is_max(ocp::OptimalControlModel) -> Bool
+

Return true if the criterion type of ocp is :max.

source
CTBase.is_min — Method
is_min(ocp::OptimalControlModel) -> Bool
+

Return true if the criterion type of ocp is :min.

source
CTBase.is_time_dependent — Method
is_time_dependent(ocp::OptimalControlModel) -> Bool
+

Return true if the model has been defined as time dependent.

source
CTBase.is_time_independent — Method
is_time_independent(ocp::OptimalControlModel) -> Bool
+

Return true if the model has been defined as time independent.

source
CTBase.is_variable_dependent — Method
is_variable_dependent(ocp::OptimalControlModel) -> Bool
+

Return true if the model has been defined as variable dependent.

source
CTBase.is_variable_independent — Method
is_variable_independent(ocp::OptimalControlModel) -> Bool
+

Return true if the model has been defined as variable independent.

source
CTBase.nlp_constraints! — Method
nlp_constraints!(
     ocp::OptimalControlModel
 ) -> Tuple{Tuple{Any, CTBase.var"#ξ#91", Vector{Real}}, Tuple{Any, CTBase.var"#η#92", Vector{Real}}, Tuple{Any, CTBase.var"#ψ#93", Vector{Real}}, Tuple{Any, CTBase.var"#ϕ#94", Vector{Real}}, Tuple{Any, CTBase.var"#θ#95", Vector{Real}}, Tuple{Vector{Real}, Vector{Int64}, Vector{Real}}, Tuple{Vector{Real}, Vector{Int64}, Vector{Real}}, Tuple{Vector{Real}, Vector{Int64}, Vector{Real}}}

Return a 6-tuple of tuples:

  • (ξl, ξ, ξu) are control constraints
  • (ηl, η, ηu) are state constraints
  • (ψl, ψ, ψu) are mixed constraints
  • (ϕl, ϕ, ϕu) are boundary constraints
  • (θl, θ, θu) are variable constraints
  • (ul, uind, uu) are control linear constraints of a subset of indices
  • (xl, xind, xu) are state linear constraints of a subset of indices
  • (vl, vind, vu) are variable linear constraints of a subset of indices

and update information about constraints dimensions of ocp.

Note
  • The dimensions of the state and control must be set before calling nlp_constraints!.
  • For a Fixed problem, dimensions associated with constraints on the variable are set to zero.

Example

julia> (ξl, ξ, ξu), (ηl, η, ηu), (ψl, ψ, ψu), (ϕl, ϕ, ϕu), (θl, θ, θu),
-    (ul, uind, uu), (xl, xind, xu), (vl, vind, vu) = nlp_constraints!(ocp)
source
CTBase.objective! — Method
objective!(
+    (ul, uind, uu), (xl, xind, xu), (vl, vind, vu) = nlp_constraints!(ocp)
source
CTBase.objective! — Method
objective!(
     ocp::OptimalControlModel{T<:TimeDependence, V<:VariableDependence},
     type::Symbol,
     g::Function,
@@ -161,7 +161,7 @@
     f⁰::Function,
     criterion::Symbol
 )
-

Set the criterion to the function g and f⁰. Type can be :bolza. Criterion is :min or :max.

Note

You can use objective! only once to define the objective.

  • The state, control and variable dimensions must be set before. Use state!, control! and variable!.
  • The times must be set before. Use time!.
  • When an element is of dimension 1, consider it as a scalar.

Example

julia> objective!(ocp, :bolza, (x0, xf) -> x0[1] + xf[2], (x, u) -> x[1]^2 + u^2) # the control is of dimension 1
source
CTBase.objective! — Method
objective!(
+

Set the criterion to the function g and f⁰. Type can be :bolza. Criterion is :min or :max.

Note

You can use objective! only once to define the objective.

  • The state, control and variable dimensions must be set before. Use state!, control! and variable!.
  • The times must be set before. Use time!.
  • When an element is of dimension 1, consider it as a scalar.

Example

julia> objective!(ocp, :bolza, (x0, xf) -> x0[1] + xf[2], (x, u) -> x[1]^2 + u^2) # the control is of dimension 1
source
CTBase.objective! — Method
objective!(
     ocp::OptimalControlModel{T<:TimeDependence, V<:VariableDependence},
     type::Symbol,
     f::Function
@@ -173,8 +173,8 @@
     criterion::Symbol
 )

Set the criterion to the function f. Type can be :mayer or :lagrange. Criterion is :min or :max.

Note

You can use objective! only once to define the objective.

  • The state, control and variable dimensions must be set before. Use state!, control! and variable!.
  • The times must be set before. Use time!.
  • When an element is of dimension 1, consider it as a scalar.

Examples

julia> objective!(ocp, :mayer, (x0, xf) -> x0[1] + xf[2])
-julia> objective!(ocp, :lagrange, (x, u) -> x[1]^2 + u^2) # the control is of dimension 1
Warning

If you set twice the objective, only the last one will be taken into account.

source
CTBase.remove_constraint! — Method
remove_constraint!(ocp::OptimalControlModel, label::Symbol)
-

Remove a labeled constraint.

Example

julia> remove_constraint!(ocp, :con)
source
CTBase.state! — Function
state!(ocp::OptimalControlModel, n::Integer)
+julia> objective!(ocp, :lagrange, (x, u) -> x[1]^2 + u^2) # the control is of dimension 1
Warning

If you set twice the objective, only the last one will be taken into account.

source
CTBase.remove_constraint! — Method
remove_constraint!(ocp::OptimalControlModel, label::Symbol)
+

Remove a labeled constraint.

Example

julia> remove_constraint!(ocp, :con)
source
CTBase.state! — Function
state!(ocp::OptimalControlModel, n::Integer)
 state!(ocp::OptimalControlModel, n::Integer, name::String)
 state!(
     ocp::OptimalControlModel,
@@ -210,7 +210,7 @@
 julia> ocp.state_dimension
 2
 julia> ocp.state_components_names
-["y₁", "y₂"]
source
CTBase.time! — Method
time!(
+["y₁", "y₂"]
source
CTBase.time! — Method
time!(
     ocp::OptimalControlModel{<:TimeDependence, VT};
     t0,
     tf,
@@ -223,7 +223,7 @@
 julia> time!(ocp, ind0=2, tf=1  ) # Free  t0 and fixed tf
 julia> time!(ocp, ind0=2, indf=3) # Free  t0 and free  tf

When you plot a solution of an optimal control problem, the name of the time variable appears. By default, the name is "t". Consider you want to set the name of the time variable to "s".

julia> time!(ocp, t0=0, tf=1, name="s") # name is a String
 # or
-julia> time!(ocp, t0=0, tf=1, name=:s ) # name is a Symbol
source
CTBase.variable! — Function
variable!(ocp::OptimalControlModel, q::Integer)
+julia> time!(ocp, t0=0, tf=1, name=:s ) # name is a Symbol
source
CTBase.variable! — Function
variable!(ocp::OptimalControlModel, q::Integer)
 variable!(
     ocp::OptimalControlModel,
     q::Integer,
@@ -236,4 +236,4 @@
     components_names::Vector{String}
 )

Define the variable dimension and possibly the names of each component.

Note

You can use variable! once to set the variable dimension when the model is NonFixed.

Examples

julia> variable!(ocp, 1, "v")
-julia> variable!(ocp, 2, "v", [ "v₁", "v₂" ])
source
+julia> variable!(ocp, 2, "v", [ "v₁", "v₂" ])source diff --git a/dev/api-parser.html b/dev/api-parser.html index ebe78c12..b4cd1f85 100644 --- a/dev/api-parser.html +++ b/dev/api-parser.html @@ -112,7 +112,7 @@ julia> constraint_type(:( v^2 + 1 ), t, t0, tf, x, u, v) :variable_fun julia> constraint_type(:( v[2]^2 + 1 ), t, t0, tf, x, u, v) -:variable_funsource
CTBase.replace_call — Method
replace_call(e, x::Symbol, t, y) -> Any
+:variable_fun
source
CTBase.replace_call — Method
replace_call(e, x::Symbol, t, y) -> Any

Replace calls in e of the form (...x...)(t) by (...y...).

Example


 julia> t = :t; t0 = 0; tf = :tf; x = :x; u = :u;
 
@@ -132,7 +132,7 @@
 :(F0(x) + u * F1(x))
 
 julia> e = :( 0.5u(t)^2 ); replace_call(e, u, t, u)
-:(0.5 * u ^ 2)
source
CTBase.replace_call — Method
replace_call(e, x::Vector{Symbol}, t, y) -> Any
+:(0.5 * u ^ 2)
source
CTBase.replace_call — Method
replace_call(e, x::Vector{Symbol}, t, y) -> Any

Replace calls in e of the form (...x1...x2...)(t) by (...y1...y2...) for all symbols x1, x2... in the vector x.

Example


 julia> t = :t; t0 = 0; tf = :tf; x = :x; u = :u;
 
@@ -143,7 +143,7 @@
 :(xx ^ 2 + uu[1])
 
 julia> e = :( ((x^2)(t0) + u[1])(t) ); replace_call(e, [ x, u ], t , [ :xx, :uu ])
-:((xx ^ 2)(t0) + uu[1])
source
CTBase.@def — Macro

Define an optimal control problem. One pass parsing of the definition.

Example

@def ocp begin
+:((xx ^ 2)(t0) + uu[1])
source
CTBase.@def — Macro

Define an optimal control problem. One pass parsing of the definition.

Example

@def ocp begin
     tf ∈ R, variable
     t ∈ [ 0, tf ], time
     x ∈ R², state
@@ -160,5 +160,5 @@
     -2 ≤ v(t) ≤ 3,      (2)
     ẋ(t) == [ v(t), u(t) ]
     tf → min
-end
source
CTBase.__OCPModel — Method
__OCPModel(args...; kwargs...) -> OptimalControlModel
-

Redirection to Model to avoid confusion with other functions Model from other packages if imported. This function is used by @def.

source
+endsource
CTBase.__OCPModel — Method
__OCPModel(args...; kwargs...) -> OptimalControlModel
+

Redirection to Model to avoid confusion with other functions Model from other packages if imported. This function is used by @def.

source
diff --git a/dev/api-plot.html b/dev/api-plot.html index 98c83cf0..92f76f4c 100644 --- a/dev/api-plot.html +++ b/dev/api-plot.html @@ -1,2 +1,2 @@ -Plot · CTBase.jl
GitHub
+Plot · CTBase.jl
GitHub
diff --git a/dev/api-print.html b/dev/api-print.html index b2cdf91d..e4eda779 100644 --- a/dev/api-print.html +++ b/dev/api-print.html @@ -82,4 +82,4 @@ ├────────┼────────┼──────────┼──────────┼───────────┼────────────┼─────────────┤ │ V │ V │ V │ X │ V │ V │ V │ ╰────────┴────────┴──────────┴──────────┴───────────┴────────────┴─────────────╯ - + diff --git a/dev/api-repl.html b/dev/api-repl.html index f9fc8e56..d90a2ff7 100644 --- a/dev/api-repl.html +++ b/dev/api-repl.html @@ -1,4 +1,4 @@ Repl · CTBase.jl
GitHub
+

Create a ct REPL.

source
CTBase.ct_repl_update_model — Method
ct_repl_update_model(e::Expr)
+

Update the model adding the expression e. It must be public since in the ct repl, this function is quoted each time an expression is parsed and is valid.

source
diff --git a/dev/api-types.html b/dev/api-types.html index 13d67198..9bef5157 100644 --- a/dev/api-types.html +++ b/dev/api-types.html @@ -1,5 +1,5 @@ -Types and functions · CTBase.jl
GitHub

Types and functions

Index

Documentation

CTBase.BoundaryConstraint — Type
struct BoundaryConstraint{variable_dependence}

Fields

  • f::Function

The default value for variable_dependence is Fixed.

Constructor

The constructor BoundaryConstraint returns a BoundaryConstraint of a function. The function must take 2 or 3 arguments (x0, xf) or (x0, xf, v), if the function is variable, it must be specified. Dependencies are specified with a boolean, variable, false by default or with a DataType, NonFixed/Fixed, Fixed by default.

Examples

julia> B = BoundaryConstraint((x0, xf) -> [xf[2]-x0[1], 2xf[1]+x0[2]^2])
+Types and functions · CTBase.jl
GitHub
Types and functions
Index
Documentation
CTBase.BoundaryConstraint — Type
struct BoundaryConstraint{variable_dependence}
Fields
  • f::Function
The default value for variable_dependence is Fixed.
Constructor
The constructor BoundaryConstraint returns a BoundaryConstraint of a function. The function must take 2 or 3 arguments (x0, xf) or (x0, xf, v), if the function is variable, it must be specified.  Dependencies are specified with a boolean, variable, false by default or with a DataType, NonFixed/Fixed, Fixed by default.
Examples
julia> B = BoundaryConstraint((x0, xf) -> [xf[2]-x0[1], 2xf[1]+x0[2]^2])
 julia> B = BoundaryConstraint((x0, xf, v) -> [v[3]+xf[2]-x0[1], v[1]-v[2]+2xf[1]+x0[2]^2], variable=true)
 julia> B = BoundaryConstraint((x0, xf, v) -> [v[3]+xf[2]-x0[1], v[1]-v[2]+2xf[1]+x0[2]^2], NonFixed)
Warning
When the state is of dimension 1, consider x0 and xf as a scalar. When the constraint is dimension 1, return a scalar.
Call
The call returns the evaluation of the BoundaryConstraint for given values. If a variable is given for a non variable dependent boundary constraint, it will be ignored.
Examples
julia> B = BoundaryConstraint((x0, xf) -> [xf[2]-x0[1], 2xf[1]+x0[2]^2])
 julia> B([0, 0], [1, 1])
@@ -9,7 +9,7 @@
 [1, 2]
 julia> B = BoundaryConstraint((x0, xf, v) -> [v[3]+xf[2]-x0[1], v[1]-v[2]+2xf[1]+x0[2]^2], variable=true)
 julia> B([0, 0], [1, 1], [1, 2, 3])
-[4, 1]
source
CTBase.ControlConstraint — Type
struct ControlConstraint{time_dependence, variable_dependence}
Fields
  • f::Function
Similar to VectorField in the usage, but the dimension of the output of the function f is arbitrary.
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor ControlConstraint returns a ControlConstraint of a function. The function must take 1 to 3 arguments, u to (t, u, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> IncorrectArgument ControlConstraint(u -> [u[1]^2, 2u[2]], Int64)
+[4, 1]
source
CTBase.ControlConstraint — Type
struct ControlConstraint{time_dependence, variable_dependence}
Fields
  • f::Function
Similar to VectorField in the usage, but the dimension of the output of the function f is arbitrary.
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor ControlConstraint returns a ControlConstraint of a function. The function must take 1 to 3 arguments, u to (t, u, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> IncorrectArgument ControlConstraint(u -> [u[1]^2, 2u[2]], Int64)
 julia> IncorrectArgument ControlConstraint(u -> [u[1]^2, 2u[2]], Int64)
 julia> C = ControlConstraint(u -> [u[1]^2, 2u[2]], Autonomous, Fixed)
 julia> C = ControlConstraint((u, v) -> [u[1]^2, 2u[2]+v[3]], Autonomous, NonFixed)
@@ -37,7 +37,7 @@
 [2, -2]
 julia> C = ControlConstraint((t, u, v) -> [t+u[1]^2, 2u[2]+v[3]], autonomous=false, variable=true)
 julia> C(1, [1, -1], [1, 2, 3])
-[2, 1]
source
CTBase.ControlLaw — Type
struct ControlLaw{time_dependence, variable_dependence}
Fields
  • f::Function
Similar to Hamiltonian in the usage, but the dimension of the output of the function f is arbitrary.
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor ControlLaw returns a ControlLaw of a function. The function must take 2 to 4 arguments, (x, p) to (t, x, p, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> ControlLaw((x, p) -> x[1]^2+2p[2], Int64)
+[2, 1]
source
CTBase.ControlLaw — Type
struct ControlLaw{time_dependence, variable_dependence}
Fields
  • f::Function
Similar to Hamiltonian in the usage, but the dimension of the output of the function f is arbitrary.
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor ControlLaw returns a ControlLaw of a function. The function must take 2 to 4 arguments, (x, p) to (t, x, p, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> ControlLaw((x, p) -> x[1]^2+2p[2], Int64)
 IncorrectArgument
 julia> ControlLaw((x, p) -> x[1]^2+2p[2], Int64)
 IncorrectArgument
@@ -71,7 +71,7 @@
 4
 julia> u = ControlLaw((t, x, p, v) -> t+x[1]^2+2p[2]+v[3], autonomous=false, variable=true)
 julia> u(1, [1, 0], [0, 1], [1, 2, 3])
-7
source
CTBase.Dynamics — Type
struct Dynamics{time_dependence, variable_dependence}
Fields
  • f::Function
The default value for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor Dynamics returns a Dynamics of a function. The function must take 2 to 4 arguments, (x, u) to (t, x, u, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> Dynamics((x, u) -> [2x[2]-u^2, x[1]], Int64)
+7
source
CTBase.Dynamics — Type
struct Dynamics{time_dependence, variable_dependence}
Fields
  • f::Function
The default value for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor Dynamics returns a Dynamics of a function. The function must take 2 to 4 arguments, (x, u) to (t, x, u, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> Dynamics((x, u) -> [2x[2]-u^2, x[1]], Int64)
 IncorrectArgument
 julia> Dynamics((x, u) -> [2x[2]-u^2, x[1]], Int64)
 IncorrectArgument
@@ -101,7 +101,7 @@
 [0, 1]
 julia> D = Dynamics((t, x, u, v) -> [t+2x[2]-u^2+v[3], x[1]], autonomous=false, variable=true)
 julia> D(1, [1, 0], 1, [1, 2, 3])
-[3, 1]
source
CTBase.FeedbackControl — Type
struct FeedbackControl{time_dependence, variable_dependence}
Fields
  • f::Function
Similar to VectorField in the usage, but the dimension of the output of the function f is arbitrary.
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor FeedbackControl returns a FeedbackControl of a function. The function must take 1 to 3 arguments, x to (t, x, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> FeedbackControl(x -> x[1]^2+2x[2], Int64)
+[3, 1]
source
CTBase.FeedbackControl — Type
struct FeedbackControl{time_dependence, variable_dependence}
Fields
  • f::Function
Similar to VectorField in the usage, but the dimension of the output of the function f is arbitrary.
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor FeedbackControl returns a FeedbackControl of a function. The function must take 1 to 3 arguments, x to (t, x, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> FeedbackControl(x -> x[1]^2+2x[2], Int64)
 IncorrectArgument
 julia> FeedbackControl(x -> x[1]^2+2x[2], Int64)
 IncorrectArgument
@@ -135,7 +135,7 @@
 2
 julia> u = FeedbackControl((t, x, v) -> t+x[1]^2+2x[2]+v[3], autonomous=false, variable=true)
 julia> u(1, [1, 0], [1, 2, 3])
-5
source
CTBase.Hamiltonian — Type
struct Hamiltonian{time_dependence, variable_dependence} <: AbstractHamiltonian{time_dependence, variable_dependence}
Fields
  • f::Function
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor Hamiltonian returns a Hamiltonian of a function. The function must take 2 to 4 arguments, (x, p) to (t, x, p, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> Hamiltonian((x, p) -> x + p, Int64)
+5
source
CTBase.Hamiltonian — Type
struct Hamiltonian{time_dependence, variable_dependence} <: AbstractHamiltonian{time_dependence, variable_dependence}
Fields
  • f::Function
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor Hamiltonian returns a Hamiltonian of a function. The function must take 2 to 4 arguments, (x, p) to (t, x, p, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> Hamiltonian((x, p) -> x + p, Int64)
 IncorrectArgument 
 julia> Hamiltonian((x, p) -> x + p, Int64)
 IncorrectArgument
@@ -167,7 +167,7 @@
 4
 julia> H = Hamiltonian((t, x, p, v) -> t+x[1]^2+2p[2]+v[3], autonomous=false, variable=true)
 julia> H(1, [1, 0], [0, 1], [1, 2, 3])
-7
source
CTBase.HamiltonianLift — Type
struct HamiltonianLift{time_dependence, variable_dependence} <: AbstractHamiltonian{time_dependence, variable_dependence}
Lifts
  • X::VectorField
The values for time_dependence and variable_dependence are deternimed by the values of those for the VectorField.
Constructor
The constructor HamiltonianLift returns a HamiltonianLift of a VectorField.
Examples
julia> H = HamiltonianLift(VectorField(x -> [x[1]^2, 2x[2]]))
+7
source
CTBase.HamiltonianLift — Type
struct HamiltonianLift{time_dependence, variable_dependence} <: AbstractHamiltonian{time_dependence, variable_dependence}
Lifts
  • X::VectorField
The values for time_dependence and variable_dependence are deternimed by the values of those for the VectorField.
Constructor
The constructor HamiltonianLift returns a HamiltonianLift of a VectorField.
Examples
julia> H = HamiltonianLift(VectorField(x -> [x[1]^2, 2x[2]]))
 julia> H = HamiltonianLift(VectorField((x, v) -> [x[1]^2, 2x[2]+v[3]], variable=true))
 julia> H = HamiltonianLift(VectorField((t, x) -> [t+x[1]^2, 2x[2]], autonomous=false))
 julia> H = HamiltonianLift(VectorField((t, x, v) -> [t+x[1]^2, 2x[2]+v[3]], autonomous=false, variable=true))
@@ -200,7 +200,7 @@
 3
Alternatively, it is possible to construct the HamiltonianLift from a Function being the VectorField.
julia> HL1 = HamiltonianLift((x, v) -> [x[1]^2,x[2]^2+v], autonomous=true, variable=true)
 julia> HL2 = HamiltonianLift(VectorField((x, v) -> [x[1]^2,x[2]^2+v], autonomous=true, variable=true))
 julia> HL1([1, 0], [0, 1], 1) == HL2([1, 0], [0, 1], 1)
-true
source
CTBase.HamiltonianVectorField — Type
struct HamiltonianVectorField{time_dependence, variable_dependence} <: CTBase.AbstractVectorField{time_dependence, variable_dependence}
Fields
  • f::Function
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor HamiltonianVectorField returns a HamiltonianVectorField of a function. The function must take 2 to 4 arguments, (x, p) to (t, x, p, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> HamiltonianVectorField((x, p) -> [x[1]^2+2p[2], x[2]-3p[2]^2], Int64)
+true
source
CTBase.HamiltonianVectorField — Type
struct HamiltonianVectorField{time_dependence, variable_dependence} <: CTBase.AbstractVectorField{time_dependence, variable_dependence}
Fields
  • f::Function
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor HamiltonianVectorField returns a HamiltonianVectorField of a function. The function must take 2 to 4 arguments, (x, p) to (t, x, p, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> HamiltonianVectorField((x, p) -> [x[1]^2+2p[2], x[2]-3p[2]^2], Int64)
 IncorrectArgument
 julia> HamiltonianVectorField((x, p) -> [x[1]^2+2p[2], x[2]-3p[2]^2], Int64)
 IncorrectArgument
@@ -233,7 +233,7 @@
 [4, -3]
 julia> Hv = HamiltonianVectorField((t, x, p, v) -> [t+x[1]^2+2p[2]+v[3], x[2]-3p[2]^2+v[4]], autonomous=false, variable=true)
 julia> Hv(1, [1, 0], [0, 1], [1, 2, 3, 4])
-[7, -3]
source
CTBase.Lagrange — Type
struct Lagrange{time_dependence, variable_dependence}
Fields
  • f::Function
The default value for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor Lagrange returns a Lagrange cost of a function. The function must take 2 to 4 arguments, (x, u) to (t, x, u, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> Lagrange((x, u) -> 2x[2]-u[1]^2, Int64)
+[7, -3]
source
CTBase.Lagrange — Type
struct Lagrange{time_dependence, variable_dependence}
Fields
  • f::Function
The default value for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor Lagrange returns a Lagrange cost of a function. The function must take 2 to 4 arguments, (x, u) to (t, x, u, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> Lagrange((x, u) -> 2x[2]-u[1]^2, Int64)
 IncorrectArgument
 julia> Lagrange((x, u) -> 2x[2]-u[1]^2, Int64)
 IncorrectArgument
@@ -272,7 +272,7 @@
 0
 julia> L = Lagrange((t, x, u, v) -> t+2x[2]-u[1]^2+v[3], autonomous=false, variable=true)
 julia> L(1, [1, 0], [1], [1, 2, 3])
-3
source
CTBase.Mayer — Type
struct Mayer{variable_dependence}
Fields
  • f::Function
The default value for variable_dependence is Fixed.
Constructor
The constructor Mayer returns a Mayer cost of a function. The function must take 2 or 3 arguments (x0, xf) or (x0, xf, v), if the function is variable, it must be specified.  Dependencies are specified with a boolean, variable, false by default or with a DataType, NonFixed/Fixed, Fixed by default.
Examples
julia> G = Mayer((x0, xf) -> xf[2]-x0[1])
+3
source
CTBase.Mayer — Type
struct Mayer{variable_dependence}
Fields
  • f::Function
The default value for variable_dependence is Fixed.
Constructor
The constructor Mayer returns a Mayer cost of a function. The function must take 2 or 3 arguments (x0, xf) or (x0, xf, v), if the function is variable, it must be specified.  Dependencies are specified with a boolean, variable, false by default or with a DataType, NonFixed/Fixed, Fixed by default.
Examples
julia> G = Mayer((x0, xf) -> xf[2]-x0[1])
 julia> G = Mayer((x0, xf, v) -> v[3]+xf[2]-x0[1], variable=true)
 julia> G = Mayer((x0, xf, v) -> v[3]+xf[2]-x0[1], NonFixed)
Warning
When the state is of dimension 1, consider x0 and xf as a scalar.
Call
The call returns the evaluation of the Mayer cost for given values. If a variable is given for a non variable dependent Mayer cost, it will be ignored.
Examples
julia> G = Mayer((x0, xf) -> xf[2]-x0[1])
 julia> G([0, 0], [1, 1])
@@ -282,7 +282,7 @@
 1
 julia> G = Mayer((x0, xf, v) -> v[3]+xf[2]-x0[1], variable=true)
 julia> G([0, 0], [1, 1], [1, 2, 3])
-4
source
CTBase.MixedConstraint — Type
struct MixedConstraint{time_dependence, variable_dependence}
Fields
  • f::Function
Similar to Lagrange in the usage, but the dimension of the output of the function f is arbitrary.
The default value for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor MixedConstraint returns a MixedConstraint of a function. The function must take 2 to 4 arguments, (x, u) to (t, x, u, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> MixedConstraint((x, u) -> [2x[2]-u^2, x[1]], Int64)
+4
source
CTBase.MixedConstraint — Type
struct MixedConstraint{time_dependence, variable_dependence}
Fields
  • f::Function
Similar to Lagrange in the usage, but the dimension of the output of the function f is arbitrary.
The default value for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor MixedConstraint returns a MixedConstraint of a function. The function must take 2 to 4 arguments, (x, u) to (t, x, u, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> MixedConstraint((x, u) -> [2x[2]-u^2, x[1]], Int64)
 IncorrectArgument
 julia> MixedConstraint((x, u) -> [2x[2]-u^2, x[1]], Int64)
 IncorrectArgument
@@ -318,7 +318,7 @@
 [0, 1]
 julia> M = MixedConstraint((t, x, u, v) -> [t+2x[2]-u^2+v[3], x[1]], autonomous=false, variable=true)
 julia> M(1, [1, 0], 1, [1, 2, 3])
-[3, 1]
source
CTBase.Multiplier — Type
struct Multiplier{time_dependence, variable_dependence}
Fields
  • f::Function
Similar to ControlLaw in the usage.
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor Multiplier returns a Multiplier of a function. The function must take 2 to 4 arguments, (x, p) to (t, x, p, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> Multiplier((x, p) -> x[1]^2+2p[2], Int64)
+[3, 1]
source
CTBase.Multiplier — Type
struct Multiplier{time_dependence, variable_dependence}
Fields
  • f::Function
Similar to ControlLaw in the usage.
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor Multiplier returns a Multiplier of a function. The function must take 2 to 4 arguments, (x, p) to (t, x, p, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> Multiplier((x, p) -> x[1]^2+2p[2], Int64)
 IncorrectArgument
 julia> Multiplier((x, p) -> x[1]^2+2p[2], Int64)
 IncorrectArgument
@@ -352,7 +352,7 @@
 4
 julia> μ = Multiplier((t, x, p, v) -> t+x[1]^2+2p[2]+v[3], autonomous=false, variable=true)
 julia> μ(1, [1, 0], [0, 1], [1, 2, 3])
-7
source
CTBase.OptimalControlModel — Type
mutable struct OptimalControlModel{time_dependence<:TimeDependence, variable_dependence<:VariableDependence} <: CTBase.AbstractOptimalControlModel
Fields
  • model_expression::Union{Nothing, Expr}: Default: nothing
  • initial_time::Union{Nothing, Index, Real}: Default: nothing
  • initial_time_name::Union{Nothing, String}: Default: nothing
  • final_time::Union{Nothing, Index, Real}: Default: nothing
  • final_time_name::Union{Nothing, String}: Default: nothing
  • time_name::Union{Nothing, String}: Default: nothing
  • control_dimension::Union{Nothing, Integer}: Default: nothing
  • control_components_names::Union{Nothing, Vector{String}}: Default: nothing
  • control_name::Union{Nothing, String}: Default: nothing
  • state_dimension::Union{Nothing, Integer}: Default: nothing
  • state_components_names::Union{Nothing, Vector{String}}: Default: nothing
  • state_name::Union{Nothing, String}: Default: nothing
  • variable_dimension::Union{Nothing, Integer}: Default: nothing
  • variable_components_names::Union{Nothing, Vector{String}}: Default: nothing
  • variable_name::Union{Nothing, String}: Default: nothing
  • lagrange::Union{Nothing, Lagrange}: Default: nothing
  • mayer::Union{Nothing, Mayer}: Default: nothing
  • criterion::Union{Nothing, Symbol}: Default: nothing
  • dynamics::Union{Nothing, Dynamics}: Default: nothing
  • constraints::Dict{Symbol, Tuple}: Default: Dict{Symbol, Tuple{Vararg{Any}}}()
  • dim_control_constraints::Union{Nothing, Integer}: Default: nothing
  • dim_state_constraints::Union{Nothing, Integer}: Default: nothing
  • dim_mixed_constraints::Union{Nothing, Integer}: Default: nothing
  • dim_boundary_constraints::Union{Nothing, Integer}: Default: nothing
  • dim_variable_constraints::Union{Nothing, Integer}: Default: nothing
  • dim_control_range::Union{Nothing, Integer}: Default: nothing
  • dim_state_range::Union{Nothing, Integer}: Default: nothing
  • dim_variable_range::Union{Nothing, Integer}: Default: nothing
source
CTBase.OptimalControlSolution — Type
mutable struct OptimalControlSolution <: CTBase.AbstractOptimalControlSolution
Type of an optimal control solution.
Fields
  • times::Union{Nothing, StepRangeLen, AbstractVector{<:Real}}: Default: nothing
  • initial_time_name::Union{Nothing, String}: Default: nothing
  • final_time_name::Union{Nothing, String}: Default: nothing
  • time_name::Union{Nothing, String}: Default: nothing
  • control_dimension::Union{Nothing, Integer}: Default: nothing
  • control_components_names::Union{Nothing, Vector{String}}: Default: nothing
  • control_name::Union{Nothing, String}: Default: nothing
  • control::Union{Nothing, Function}: Default: nothing
  • state_dimension::Union{Nothing, Integer}: Default: nothing
  • state_components_names::Union{Nothing, Vector{String}}: Default: nothing
  • state_name::Union{Nothing, String}: Default: nothing
  • state::Union{Nothing, Function}: Default: nothing
  • variable_dimension::Union{Nothing, Integer}: Default: nothing
  • variable_components_names::Union{Nothing, Vector{String}}: Default: nothing
  • variable_name::Union{Nothing, String}: Default: nothing
  • variable::Union{Nothing, Real, AbstractVector{<:Real}}: Default: nothing
  • costate::Union{Nothing, Function}: Default: nothing
  • objective::Union{Nothing, Real}: Default: nothing
  • iterations::Union{Nothing, Integer}: Default: nothing
  • stopping::Union{Nothing, Symbol}: Default: nothing
  • message::Union{Nothing, String}: Default: nothing
  • success::Union{Nothing, Bool}: Default: nothing
  • infos::Dict{Symbol, Any}: Default: Dict{Symbol, Any}()
source
CTBase.StateConstraint — Type
struct StateConstraint{time_dependence, variable_dependence}
Fields
  • f::Function
Similar to VectorField in the usage, but the dimension of the output of the function f is arbitrary.
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor StateConstraint returns a StateConstraint of a function. The function must take 1 to 3 arguments, x to (t, x, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> StateConstraint(x -> [x[1]^2, 2x[2]], Int64)
+7
source
CTBase.OptimalControlModel — Type
mutable struct OptimalControlModel{time_dependence<:TimeDependence, variable_dependence<:VariableDependence} <: CTBase.AbstractOptimalControlModel
Fields
  • model_expression::Union{Nothing, Expr}: Default: nothing
  • initial_time::Union{Nothing, Index, Real}: Default: nothing
  • initial_time_name::Union{Nothing, String}: Default: nothing
  • final_time::Union{Nothing, Index, Real}: Default: nothing
  • final_time_name::Union{Nothing, String}: Default: nothing
  • time_name::Union{Nothing, String}: Default: nothing
  • control_dimension::Union{Nothing, Integer}: Default: nothing
  • control_components_names::Union{Nothing, Vector{String}}: Default: nothing
  • control_name::Union{Nothing, String}: Default: nothing
  • state_dimension::Union{Nothing, Integer}: Default: nothing
  • state_components_names::Union{Nothing, Vector{String}}: Default: nothing
  • state_name::Union{Nothing, String}: Default: nothing
  • variable_dimension::Union{Nothing, Integer}: Default: nothing
  • variable_components_names::Union{Nothing, Vector{String}}: Default: nothing
  • variable_name::Union{Nothing, String}: Default: nothing
  • lagrange::Union{Nothing, Lagrange}: Default: nothing
  • mayer::Union{Nothing, Mayer}: Default: nothing
  • criterion::Union{Nothing, Symbol}: Default: nothing
  • dynamics::Union{Nothing, Dynamics}: Default: nothing
  • constraints::Dict{Symbol, Tuple}: Default: Dict{Symbol, Tuple{Vararg{Any}}}()
  • dim_control_constraints::Union{Nothing, Integer}: Default: nothing
  • dim_state_constraints::Union{Nothing, Integer}: Default: nothing
  • dim_mixed_constraints::Union{Nothing, Integer}: Default: nothing
  • dim_boundary_constraints::Union{Nothing, Integer}: Default: nothing
  • dim_variable_constraints::Union{Nothing, Integer}: Default: nothing
  • dim_control_range::Union{Nothing, Integer}: Default: nothing
  • dim_state_range::Union{Nothing, Integer}: Default: nothing
  • dim_variable_range::Union{Nothing, Integer}: Default: nothing
source
CTBase.OptimalControlSolution — Type
mutable struct OptimalControlSolution <: CTBase.AbstractOptimalControlSolution
Type of an optimal control solution.
Fields
  • times::Union{Nothing, StepRangeLen, AbstractVector{<:Real}}: Default: nothing
  • initial_time_name::Union{Nothing, String}: Default: nothing
  • final_time_name::Union{Nothing, String}: Default: nothing
  • time_name::Union{Nothing, String}: Default: nothing
  • control_dimension::Union{Nothing, Integer}: Default: nothing
  • control_components_names::Union{Nothing, Vector{String}}: Default: nothing
  • control_name::Union{Nothing, String}: Default: nothing
  • control::Union{Nothing, Function}: Default: nothing
  • state_dimension::Union{Nothing, Integer}: Default: nothing
  • state_components_names::Union{Nothing, Vector{String}}: Default: nothing
  • state_name::Union{Nothing, String}: Default: nothing
  • state::Union{Nothing, Function}: Default: nothing
  • variable_dimension::Union{Nothing, Integer}: Default: nothing
  • variable_components_names::Union{Nothing, Vector{String}}: Default: nothing
  • variable_name::Union{Nothing, String}: Default: nothing
  • variable::Union{Nothing, Real, AbstractVector{<:Real}}: Default: nothing
  • costate::Union{Nothing, Function}: Default: nothing
  • objective::Union{Nothing, Real}: Default: nothing
  • iterations::Union{Nothing, Integer}: Default: nothing
  • stopping::Union{Nothing, Symbol}: Default: nothing
  • message::Union{Nothing, String}: Default: nothing
  • success::Union{Nothing, Bool}: Default: nothing
  • infos::Dict{Symbol, Any}: Default: Dict{Symbol, Any}()
source
CTBase.StateConstraint — Type
struct StateConstraint{time_dependence, variable_dependence}
Fields
  • f::Function
Similar to VectorField in the usage, but the dimension of the output of the function f is arbitrary.
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor StateConstraint returns a StateConstraint of a function. The function must take 1 to 3 arguments, x to (t, x, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> StateConstraint(x -> [x[1]^2, 2x[2]], Int64)
 IncorrectArgument
 julia> StateConstraint(x -> [x[1]^2, 2x[2]], Int64)
 IncorrectArgument
@@ -386,9 +386,9 @@
 [2, -2]
 julia> S = StateConstraint((t, x, v) -> [t+x[1]^2, 2x[2]+v[3]], autonomous=false, variable=true)
 julia>  S(1, [1, -1], [1, 2, 3])
-[2, 1]
source
CTBase.VariableConstraint — Type
struct VariableConstraint
Fields
  • f::Function
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor VariableConstraint returns a VariableConstraint of a function. The function must take 1 argument, v.
Examples
julia> V = VariableConstraint(v -> [v[1]^2, 2v[2]])
Warning
When the variable is of dimension 1, consider v as a scalar.
Call
The call returns the evaluation of the VariableConstraint for given values.
Examples
julia> V = VariableConstraint(v -> [v[1]^2, 2v[2]])
+[2, 1]
source
CTBase.VariableConstraint — Type
struct VariableConstraint
Fields
  • f::Function
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor VariableConstraint returns a VariableConstraint of a function. The function must take 1 argument, v.
Examples
julia> V = VariableConstraint(v -> [v[1]^2, 2v[2]])
Warning
When the variable is of dimension 1, consider v as a scalar.
Call
The call returns the evaluation of the VariableConstraint for given values.
Examples
julia> V = VariableConstraint(v -> [v[1]^2, 2v[2]])
 julia> V([1, -1])
-[1, -2]
source
CTBase.VectorField — Type
struct VectorField{time_dependence, variable_dependence} <: CTBase.AbstractVectorField{time_dependence, variable_dependence}
Fields
  • f::Function
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor VectorField returns a VectorField of a function. The function must take 1 to 3 arguments, x to (t, x, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> VectorField(x -> [x[1]^2, 2x[2]], Int64)
+[1, -2]
source
CTBase.VectorField — Type
struct VectorField{time_dependence, variable_dependence} <: CTBase.AbstractVectorField{time_dependence, variable_dependence}
Fields
  • f::Function
The default values for time_dependence and variable_dependence are Autonomous and Fixed respectively.
Constructor
The constructor VectorField returns a VectorField of a function. The function must take 1 to 3 arguments, x to (t, x, v), if the function is variable or non autonomous, it must be specified.  Dependencies are specified either with :
  • booleans, autonomous and variable, respectively true and false by default 
  • DataType, Autonomous/NonAutonomous and NonFixed/Fixed, respectively Autonomous and Fixed by default.
Examples
julia> VectorField(x -> [x[1]^2, 2x[2]], Int64)
 IncorrectArgument
 julia> VectorField(x -> [x[1]^2, 2x[2]], Int64)
 IncorrectArgument
@@ -421,4 +421,4 @@
 [2, -2]
 julia> V = VectorField((t, x, v) -> [t+x[1]^2, 2x[2]+v[3]], autonomous=false, variable=true)
 julia> V(1, [1, -1], [1, 2, 3])
-[2, 1]
source

+[2, 1]
source
diff --git a/dev/api-utils.html b/dev/api-utils.html index 2e94a4ad..f1b33d66 100644 --- a/dev/api-utils.html +++ b/dev/api-utils.html @@ -1,11 +1,11 @@ -Utils · CTBase.jl
GitHub

Utils

Index

Documentation

+Utils · CTBase.jl
GitHub

Utils

Index

Documentation

diff --git a/dev/index.html b/dev/index.html index 6c965d14..cdb27936 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,2 +1,2 @@ -Introduction · CTBase.jl
GitHub

CTBase.jl

The CTBase.jl package is part of the control-toolbox ecosystem.

Install

To install a package from the control-toolbox ecosystem, please visit the installation page.

You may find in this package, some tools to:

You may find the imported modules and exported names but also the different types of exceptions and functions specific to the control-toolbox ecosystem.

+Introduction · CTBase.jl
GitHub

CTBase.jl

The CTBase.jl package is part of the control-toolbox ecosystem.

Install

To install a package from the control-toolbox ecosystem, please visit the installation page.

You may find in this package, some tools to:

You may find the imported modules and exported names but also the different types of exceptions and functions specific to the control-toolbox ecosystem.