138 lines
3.3 KiB
Python
138 lines
3.3 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import math
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from mpc_func import MpcController
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from control import matlab
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class FirstOrderSystem():
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"""FirstOrderSystemWithStates
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Attributes
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-----------
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states : float
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system states
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A : numpy.ndarray
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system matrix
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B : numpy.ndarray
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control matrix
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C : numpy.ndarray
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observation matrix
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history_state : list
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time history of state
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"""
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def __init__(self, A, B, C, D=None, init_states=None):
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"""
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Parameters
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-----------
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A : numpy.ndarray
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system matrix
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B : numpy.ndarray
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control matrix
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C : numpy.ndarray
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observation matrix
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C : numpy.ndarray
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directly matrix
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init_state : float, optional
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initial state of system default is None
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history_xs : list
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time history of system states
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"""
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if init_states is not None:
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self.states = init_states
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self.A = A
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self.B = B
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self.C = C
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if D is not None:
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self.D = D
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self.xs = np.zeros(self.A.shape[0])
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self.history_xs = [init_states]
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def update_state(self, us, dt=0.01):
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"""calculating input
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Parameters
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------------
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u : float
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input of system in some cases this means the reference
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dt : float in seconds, optional
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sampling time of simulation, default is 0.01 [s]
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"""
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temp = self.xs.reshape(-1, 1)
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# solve Runge-Kutta
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k0 = dt * (np.dot(self.A, temp) + np.dot(self.B, us))
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k1 = dt * (np.dot(self.A, temp + k0/2.) + np.dot(self.B, us))
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k2 = dt * (np.dot(self.A, temp + k1/2.) + np.dot(self.B, us))
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k3 = dt * (np.dot(self.A, temp + k2) + np.dot(self.B, us))
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self.xs += ((k0 + 2 * k1 + 2 * k2 + k3) / 6.).flatten()
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# for oylar
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# self.state += k0
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# save
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self.history_xs.append(self.xs)
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def main():
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dt = 0.01
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simulation_time = 100 # in seconds
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iteration_num = int(simulation_time / dt)
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# you must be care about this matrix
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# these A and B are for continuos system if you want to use discret system matrix please skip this step
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tau = 0.53
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A = np.array([[1./tau, 0., 0., 0.],
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[0., 1./tau, 0., 0.],
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[1., 0., 0., 0.],
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[1., 0., 0., 0.]])
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B = np.array([[1./tau, 0.],
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[0., 1./tau],
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[0., 0.],
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[0., 0.]])
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C = np.eye(4)
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D = np.zeros((4, 2))
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# make simulator with coninuous matrix
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plant = FirstOrderSystem(A, B, C)
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# create system
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sysc = matlab.ss(A, B, C, D)
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# discrete system
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sysd = matlab.c2d(sysc, dt)
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Ad = sysd.A
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Bd = sysd.B
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# evaluation function weight
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Q = np.diag([1., 1., 1., 1.])
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R = np.diag([1., 1.])
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pre_step = 3
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# make controller with discreted matrix
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controller = MpcController(Ad, Bd, Q, R, pre_step)
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controller.initialize_controller()
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xs = np.array([0., 0., 0., 0.])
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for i in range(iteration_num):
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controller.calc_input(xs)
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# states = plant.states
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# controller.calc_input
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if __name__ == "__main__":
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main()
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