254 lines
9.0 KiB
Python
254 lines
9.0 KiB
Python
import numpy as np
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import osqp
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from scipy import sparse
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import matplotlib.pyplot as plt
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from time import time
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# Colors
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PREDICTION = '#BA4A00'
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##################
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# MPC Controller #
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##################
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class MPC:
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def __init__(self, model, N, Q, R, QN, StateConstraints, InputConstraints):
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"""
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Constructor for the Model Predictive Controller.
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:param model: bicycle model object to be controlled
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:param T: time horizon | int
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:param Q: state cost matrix
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:param R: input cost matrix
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:param QN: final state cost matrix
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:param StateConstraints: dictionary of state constraints
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:param InputConstraints: dictionary of input constraints
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"""
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# Parameters
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self.N = N # horizon
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self.Q = Q # weight matrix state vector
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self.R = R # weight matrix input vector
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self.QN = QN # weight matrix terminal
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# Model
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self.model = model
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# Dimensions
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self.nx = self.model.n_states
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self.nu = 2
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# Constraints
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self.state_constraints = StateConstraints
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self.input_constraints = InputConstraints
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# Maximum lateral acceleration
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self.ay_max = 5.0
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# Current control and prediction
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self.current_prediction = None
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# Counter for old control signals in case of infeasible problem
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self.infeasibility_counter = 0
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# Current control signals
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self.current_control = np.ones((self.nu*self.N))
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# Initialize Optimization Problem
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self.optimizer = osqp.OSQP()
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def _init_problem(self):
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"""
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Initialize optimization problem for current time step.
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"""
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# Constraints
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umin = self.input_constraints['umin']
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umax = self.input_constraints['umax']
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xmin = self.state_constraints['xmin']
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xmax = self.state_constraints['xmax']
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# LTV System Matrices
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A = np.zeros((self.nx * (self.N + 1), self.nx * (self.N + 1)))
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B = np.zeros((self.nx * (self.N + 1), self.nu * (self.N)))
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# Reference vector for state and input variables
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ur = np.zeros(self.nu*self.N)
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xr = np.array([0.0, 0.0, 0.0])
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# Offset for equality constraint (due to B * (u - ur))
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uq = np.zeros(self.N * self.nx)
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# Dynamic state constraints
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xmin_dyn = np.kron(np.ones(self.N + 1), xmin)
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xmax_dyn = np.kron(np.ones(self.N + 1), xmax)
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# Dynamic input constraints
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umax_dyn = np.kron(np.ones(self.N), umax)
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# Get curvature predictions from previous control signals
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kappa_pred = np.tan(np.array(self.current_control[3::] +
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self.current_control[-1:])) / self.model.l
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# Iterate over horizon
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for n in range(self.N):
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# Get information about current waypoint
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current_waypoint = self.model.reference_path.get_waypoint(self.model.wp_id + n)
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next_waypoint = self.model.reference_path.get_waypoint(self.model.wp_id + n + 1)
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delta_s = next_waypoint - current_waypoint
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kappa_ref = current_waypoint.kappa
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v_ref = current_waypoint.v_ref
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# Compute LTV matrices
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f, A_lin, B_lin = self.model.linearize(v_ref, kappa_ref, delta_s)
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A[(n+1) * self.nx: (n+2)*self.nx, n * self.nx:(n+1)*self.nx] = A_lin
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B[(n+1) * self.nx: (n+2)*self.nx, n * self.nu:(n+1)*self.nu] = B_lin
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# Set reference for input signal
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ur[n*self.nu:(n+1)*self.nu] = np.array([v_ref, kappa_ref])
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# Compute equality constraint offset (B*ur)
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uq[n * self.nx:(n+1)*self.nx] = B_lin.dot(np.array
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([v_ref, kappa_ref])) - f
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# Constrain maximum speed based on predicted car curvature
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vmax_dyn = np.sqrt(self.ay_max / (np.abs(kappa_pred[n]) + 1e-12))
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if vmax_dyn < umax_dyn[self.nu*n]:
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umax_dyn[self.nu*n] = vmax_dyn
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# Compute dynamic constraints on e_y
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ub, lb = self.model.reference_path.update_path_constraints(
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self.model.wp_id+1, self.N, 2*self.model.safety_margin[1],
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self.model.safety_margin[1])
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xmin_dyn[0] = self.model.spatial_state.e_y
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xmax_dyn[0] = self.model.spatial_state.e_y
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xmin_dyn[self.nx::self.nx] = lb
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xmax_dyn[self.nx::self.nx] = ub
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# Get equality matrix
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Ax = sparse.kron(sparse.eye(self.N + 1),
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-sparse.eye(self.nx)) + sparse.csc_matrix(A)
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Bu = sparse.csc_matrix(B)
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Aeq = sparse.hstack([Ax, Bu])
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# Get inequality matrix
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Aineq = sparse.eye((self.N + 1) * self.nx + self.N * self.nu)
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# Combine constraint matrices
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A = sparse.vstack([Aeq, Aineq], format='csc')
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# Get upper and lower bound vectors for equality constraints
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lineq = np.hstack([xmin_dyn,
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np.kron(np.ones(self.N), umin)])
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uineq = np.hstack([xmax_dyn, umax_dyn])
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# Get upper and lower bound vectors for inequality constraints
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x0 = np.array(self.model.spatial_state[:])
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leq = np.hstack([-x0, uq])
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ueq = leq
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# Combine upper and lower bound vectors
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l = np.hstack([leq, lineq])
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u = np.hstack([ueq, uineq])
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# Set cost matrices
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P = sparse.block_diag([sparse.kron(sparse.eye(self.N), self.Q), self.QN,
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sparse.kron(sparse.eye(self.N), self.R)], format='csc')
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q = np.hstack(
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[np.kron(np.ones(self.N), -self.Q.dot(xr)), -self.QN.dot(xr),
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-np.tile(np.array([self.R.A[0, 0], self.R.A[1, 1]]), self.N) * ur])
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# Initialize optimizer
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self.optimizer = osqp.OSQP()
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self.optimizer.setup(P=P, q=q, A=A, l=l, u=u, verbose=False)
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def get_control(self):
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"""
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Get control signal given the current position of the car. Solves a
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finite time optimization problem based on the linearized car model.
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"""
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# Number of state variables
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nx = self.model.n_states
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nu = 2
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# Update current waypoint
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self.model.get_current_waypoint()
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# Update spatial state
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self.model.spatial_state = self.model.t2s()
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# Initialize optimization problem
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self._init_problem()
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# Solve optimization problem
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dec = self.optimizer.solve()
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try:
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# Get control signals
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control_signals = np.array(dec.x[-self.N*nu:])
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control_signals[1::2] = np.arctan(control_signals[1::2] * self.model.l)
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v = control_signals[0]
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delta = control_signals[1]
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# Update control signals
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self.current_control = control_signals
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# Get predicted spatial states
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x = np.reshape(dec.x[:(self.N+1)*nx], (self.N+1, nx))
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# Update predicted temporal states
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self.current_prediction = self.update_prediction(delta, x)
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# Get current control signal
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u = np.array([v, delta])
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# if problem solved, reset infeasibility counter
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self.infeasibility_counter = 0
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except:
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print('Infeasible problem. Previously predicted'
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' control signal used!')
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id = nu * (self.infeasibility_counter + 1)
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u = np.array(self.current_control[id:id+2])
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# increase infeasibility counter
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self.infeasibility_counter += 1
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if self.infeasibility_counter == (self.N - 1):
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print('No control signal computed!')
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exit(1)
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return u
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def update_prediction(self, u, spatial_state_prediction):
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"""
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Transform the predicted states to predicted x and y coordinates.
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Mainly for visualization purposes.
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:param spatial_state_prediction: list of predicted state variables
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:return: lists of predicted x and y coordinates
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"""
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# containers for x and y coordinates of predicted states
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x_pred, y_pred = [], []
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# get current waypoint ID
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#print('#########################')
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for n in range(2, self.N):
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associated_waypoint = self.model.reference_path.get_waypoint(self.model.wp_id+n)
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predicted_temporal_state = self.model.s2t(associated_waypoint,
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spatial_state_prediction[n, :])
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#print(spatial_state_prediction[n, 2])
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#print('delta: ', u)
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#print('e_y: ', spatial_state_prediction[n, 0])
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#print('e_psi: ', spatial_state_prediction[n, 1])
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#print('t: ', spatial_state_prediction[n, 2])
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#print('+++++++++++++++++++++++')
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x_pred.append(predicted_temporal_state.x)
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y_pred.append(predicted_temporal_state.y)
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return x_pred, y_pred
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def show_prediction(self):
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"""
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Display predicted car trajectory in current axis.
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"""
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plt.scatter(self.current_prediction[0], self.current_prediction[1],
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c=PREDICTION, s=5)
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