Renaming q_lin to q_mpc

benchmarking_sim/cartpole/config_overrides/linear_mpsc_cartpole_stab_100.yaml

@@ -1,9 +1,9 @@
 safety_filter: linear_mpsc
 sf_config:
   # LQR controller parameters
-  r_lin:
+  r_mpc:
     - 0.1
-  q_lin:
+  q_mpc:
     - 1
     - 0.1
     - 1

examples/hpo/cartpole/config_overrides/linear_mpsc_cartpole_stab_100.yaml

@@ -1,9 +1,9 @@
 safety_filter: linear_mpsc
 sf_config:
   # LQR controller parameters
-  r_lin:
+  r_mpc:
     - 0.1
-  q_lin:
+  q_mpc:
     - 1
     - 0.1
     - 1

examples/mpsc/config_overrides/cartpole/linear_mpsc_cartpole.yaml

@@ -1,9 +1,9 @@
 safety_filter: linear_mpsc
 sf_config:
   # LQR controller parameters
-  r_lin:
+  r_mpc:
     - 0.1
-  q_lin:
+  q_mpc:
     - 1
     - 1
     - 1

examples/mpsc/config_overrides/cartpole/lqr_cartpole.yaml

@@ -1,9 +1,9 @@
 algo: lqr
 algo_config:
   # Cost parameters
-  r_lin:
+  r_mpc:
     - 0.1
-  q_lin:
+  q_mpc:
     - 1
     - 1
     - 1

examples/mpsc/config_overrides/quadrotor_2D/linear_mpsc_quadrotor_2D.yaml

@@ -1,9 +1,9 @@
 safety_filter: linear_mpsc
 sf_config:
   # LQR controller parameters
-  r_lin:
+  r_mpc:
     - 0.1
-  q_lin:
+  q_mpc:
     - 1
     - 1
     - 1

examples/mpsc/config_overrides/quadrotor_2D/lqr_quadrotor_2D.yaml

@@ -1,9 +1,9 @@
 algo: lqr
 algo_config:
   # Cost parameters
-  r_lin:
+  r_mpc:
     - 0.1
-  q_lin:
+  q_mpc:
     - 1
     - 1
     - 1

examples/mpsc/config_overrides/quadrotor_3D/linear_mpsc_quadrotor_3D.yaml

@@ -1,9 +1,9 @@
 safety_filter: linear_mpsc
 sf_config:
   # LQR controller parameters
-  r_lin:
+  r_mpc:
     - 0.1
-  q_lin:
+  q_mpc:
     - 0.01
     - 0.01
     - 0.01

safe_control_gym/safety_filters/mpsc/linear_mpsc.py

@@ -31,8 +31,8 @@ class LINEAR_MPSC(MPSC):
     def __init__(self,
                  env_func,
                  horizon: int = 10,
-                 q_lin: list = None,
-                 r_lin: list = None,
+                 q_mpc: list = None,
+                 r_mpc: list = None,
                  integration_algo: str = 'rk4',
                  n_samples: int = 600,
                  n_samples_terminal_set: int = 100,
@@ -51,7 +51,7 @@ def __init__(self,
         Args:
             env_func (partial gym.Env): Environment for the task.
             horizon (int): The MPC horizon.
-            q_lin, r_lin (list): Q and R gain matrices for linear controller.
+            q_mpc, r_mpc (list): Q and R gain matrices for linear controller.
             integration_algo (str): The algorithm used for integrating the dynamics,
                 either 'LTI', 'rk4', 'rk', or 'cvodes'.
             n_samples (int): Number of samples to create W set.
@@ -71,7 +71,7 @@ def __init__(self,
             if key != 'self' and key != 'kwargs' and '__' not in key:
                 self.__dict__[key] = value
 
-        super().__init__(env_func, horizon, q_lin, r_lin, integration_algo, warmstart, additional_constraints, use_terminal_set, cost_function, mpsc_cost_horizon, decay_factor, **kwargs)
+        super().__init__(env_func, horizon, q_mpc, r_mpc, integration_algo, warmstart, additional_constraints, use_terminal_set, cost_function, mpsc_cost_horizon, decay_factor, **kwargs)
 
         self.terminal_set_verts = None
 

safe_control_gym/safety_filters/mpsc/mpsc.py

@@ -27,8 +27,8 @@ class MPSC(BaseSafetyFilter, ABC):
     def __init__(self,
                  env_func,
                  horizon: int = 10,
-                 q_lin: list = None,
-                 r_lin: list = None,
+                 q_mpc: list = None,
+                 r_mpc: list = None,
                  integration_algo: str = 'rk4',
                  warmstart: bool = True,
                  additional_constraints: list = None,
@@ -44,7 +44,7 @@ def __init__(self,
         Args:
             env_func (partial BenchmarkEnv): Environment for the task.
             horizon (int): The MPC horizon.
-            q_lin, r_lin (list): Q and R gain matrices for linear controller.
+            q_mpc, r_mpc (list): Q and R gain matrices for linear controller.
             integration_algo (str): The algorithm used for integrating the dynamics,
                 either 'LTI', 'rk4', 'rk', or 'cvodes'.
             warmstart (bool): If the previous MPC soln should be used to warmstart the next mpc step.
@@ -74,8 +74,8 @@ def __init__(self,
         # Setup attributes.
         self.reset()
         self.dt = self.model.dt
-        self.Q = get_cost_weight_matrix(q_lin, self.model.nx)
-        self.R = get_cost_weight_matrix(r_lin, self.model.nu)
+        self.Q = get_cost_weight_matrix(q_mpc, self.model.nx)
+        self.R = get_cost_weight_matrix(r_mpc, self.model.nu)
 
         self.X_EQ = np.zeros(self.model.nx)
         self.U_EQ = self.model.U_EQ
@@ -244,7 +244,7 @@ def solve_acados_optimization(self,
         try:
             action = ocp_solver.solve_for_x0(x0_bar=obs)
             self.cost_prev = ocp_solver.get_cost()
-            self.slack_prev = np.zeros((self.horizon, self.p))
+            self.slack_prev = np.zeros((self.horizon, self.model.nx + self.model.nu))
             x_val = np.zeros((self.horizon + 1, self.model.nx))
             u_val = np.zeros((self.horizon, self.model.nu))
             for i in range(self.horizon):

safe_control_gym/safety_filters/mpsc/mpsc.yaml

@@ -1,7 +1,7 @@
 # LQR controller parameters
-r_lin:
+r_mpc:
   - 1.
-q_lin:
+q_mpc:
   - 1.
 
 # MPC Parameters

safe_control_gym/safety_filters/mpsc/nl_mpsc.py

@@ -35,8 +35,8 @@ class NL_MPSC(MPSC):
     def __init__(self,
                  env_func,
                  horizon: int = 10,
-                 q_lin: list = None,
-                 r_lin: list = None,
+                 q_mpc: list = None,
+                 r_mpc: list = None,
                  integration_algo: str = 'rk4',
                  warmstart: bool = True,
                  additional_constraints: list = None,
@@ -66,7 +66,7 @@ def __init__(self,
         '''
 
         self.model_bias = None
-        super().__init__(env_func, horizon, q_lin, r_lin, integration_algo, warmstart, additional_constraints, use_terminal_set, cost_function, mpsc_cost_horizon, decay_factor, **kwargs)
+        super().__init__(env_func, horizon, q_mpc, r_mpc, integration_algo, warmstart, additional_constraints, use_terminal_set, cost_function, mpsc_cost_horizon, decay_factor, **kwargs)
 
         self.n_samples = n_samples
         self.soften_constraints = soften_constraints