assignment2.py

import numpy as np
from sim.sim2d import sim_run

# Simulator options.
options = {}
options['FIG_SIZE'] = [8,8]
options['OBSTACLES'] = False

class ModelPredictiveControl:
    def __init__(self):
        self.horizon = 15
        self.dt = 0.2

        # Reference or set point the controller will achieve.
        self.reference1 = [10, 10, 0]
        self.reference2 = None
        self.reference2 = [10, 2, 2.6*3.14/2]

    def plant_model(self,prev_state, dt, pedal, steering):
        x_t = prev_state[0]
        y_t = prev_state[1]
        psi_t = prev_state[2]
        v_t = prev_state[3]

        a_t = pedal
        x_t = x_t + np.cos(psi_t) * v_t * dt
        y_t = y_t + np.sin(psi_t) * v_t * dt
        v_t = v_t + a_t * dt - v_t/25
        psi_t = psi_t + v_t * (np.tan(steering)/2.5) * dt
        return [x_t, y_t, psi_t, v_t]

    def cost_function(self,u, *args):
        state = args[0]
        ref = args[1]
        cost = 0.0
        for i in range(0, self.horizon):
            state = self.plant_model(state, self.dt, u[i*2], u[i*2+1])
            # Distance cost
            distance_cost = np.sqrt( ((ref[0] - state[0]) ** 2) + ((ref[1] - state[1]) ** 2) )
            # Angle cost
            angle_cost = (ref[2] - state[2]) ** 2
            # Acceleration cost
            acc_cost = 0
            cost += distance_cost + acc_cost + angle_cost
        return cost

sim_run(options, ModelPredictiveControl)