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195 | # steering-gainsched.py - gain scheduled control for vehicle steering
# RMM, 8 May 2019
#
# This file works through Example 1.1 in the "Optimization-Based Control"
# course notes by Richard Murray (avaliable at http://fbsbook.org, in the
# optimization-based control supplement). It is intended to demonstrate the
# functionality for nonlinear input/output systems in the python-control
# package.
import numpy as np
import control as ct
from cmath import sqrt
import matplotlib.pyplot as mpl
#
# Vehicle steering dynamics
#
# The vehicle dynamics are given by a simple bicycle model. We take the state
# of the system as (x, y, theta) where (x, y) is the position of the vehicle
# in the plane and theta is the angle of the vehicle with respect to
# horizontal. The vehicle input is given by (v, phi) where v is the forward
# velocity of the vehicle and phi is the angle of the steering wheel. The
# model includes saturation of the vehicle steering angle.
#
# System state: x, y, theta
# System input: v, phi
# System output: x, y
# System parameters: wheelbase, maxsteer
#
def vehicle_update(t, x, u, params):
# Get the parameters for the model
l = params.get('wheelbase', 3.) # vehicle wheelbase
phimax = params.get('maxsteer', 0.5) # max steering angle (rad)
# Saturate the steering input
phi = np.clip(u[1], -phimax, phimax)
# Return the derivative of the state
return np.array([
np.cos(x[2]) * u[0], # xdot = cos(theta) v
np.sin(x[2]) * u[0], # ydot = sin(theta) v
(u[0] / l) * np.tan(phi) # thdot = v/l tan(phi)
])
def vehicle_output(t, x, u, params):
return x # return x, y, theta (full state)
# Define the vehicle steering dynamics as an input/output system
vehicle = ct.NonlinearIOSystem(
vehicle_update, vehicle_output, states=3, name='vehicle',
inputs=('v', 'phi'),
outputs=('x', 'y', 'theta'))
#
# Gain scheduled controller
#
# For this system we use a simple schedule on the forward vehicle velocity and
# place the poles of the system at fixed values. The controller takes the
# current vehicle position and orientation plus the velocity velocity as
# inputs, and returns the velocity and steering commands.
#
# System state: none
# System input: ex, ey, etheta, vd, phid
# System output: v, phi
# System parameters: longpole, latpole1, latpole2
#
def control_output(t, x, u, params):
# Get the controller parameters
longpole = params.get('longpole', -2.)
latpole1 = params.get('latpole1', -1/2 + sqrt(-7)/2)
latpole2 = params.get('latpole2', -1/2 - sqrt(-7)/2)
l = params.get('wheelbase', 3)
# Extract the system inputs
ex, ey, etheta, vd, phid = u
# Determine the controller gains
alpha1 = -np.real(latpole1 + latpole2)
alpha2 = np.real(latpole1 * latpole2)
# Compute and return the control law
v = -longpole * ex # Note: no feedfwd (to make plot interesting)
if vd != 0:
phi = phid + (alpha1 * l) / vd * ey + (alpha2 * l) / vd * etheta
else:
# We aren't moving, so don't turn the steering wheel
phi = phid
return np.array([v, phi])
# Define the controller as an input/output system
controller = ct.NonlinearIOSystem(
None, control_output, name='controller', # static system
inputs=('ex', 'ey', 'etheta', 'vd', 'phid'), # system inputs
outputs=('v', 'phi') # system outputs
)
#
# Reference trajectory subsystem
#
# The reference trajectory block generates a simple trajectory for the system
# given the desired speed (vref) and lateral position (yref). The trajectory
# consists of a straight line of the form (vref * t, yref, 0) with nominal
# input (vref, 0).
#
# System state: none
# System input: vref, yref
# System output: xd, yd, thetad, vd, phid
# System parameters: none
#
def trajgen_output(t, x, u, params):
vref, yref = u
return np.array([vref * t, yref, 0, vref, 0])
# Define the trajectory generator as an input/output system
trajgen = ct.NonlinearIOSystem(
None, trajgen_output, name='trajgen',
inputs=('vref', 'yref'),
outputs=('xd', 'yd', 'thetad', 'vd', 'phid'))
#
# System construction
#
# The input to the full closed loop system is the desired lateral position and
# the desired forward velocity. The output for the system is taken as the
# full vehicle state plus the velocity of the vehicle. The following diagram
# summarizes the interconnections:
#
# +---------+ +---------------> v
# | | |
# [ yref ] | v |
# [ ] ---> trajgen -+-+-> controller -+-> vehicle -+-> [x, y, theta]
# [ vref ] ^ |
# | |
# +----------- [-1] -----------+
#
# We construct the system using the InterconnectedSystem constructor and using
# signal labels to keep track of everything.
steering = ct.InterconnectedSystem(
# List of subsystems
(trajgen, controller, vehicle), name='steering',
# Interconnections between subsystems
connections=(
('controller.ex', 'trajgen.xd', '-vehicle.x'),
('controller.ey', 'trajgen.yd', '-vehicle.y'),
('controller.etheta', 'trajgen.thetad', '-vehicle.theta'),
('controller.vd', 'trajgen.vd'),
('controller.phid', 'trajgen.phid'),
('vehicle.v', 'controller.v'),
('vehicle.phi', 'controller.phi')
),
# System inputs
inplist=['trajgen.vref', 'trajgen.yref'],
inputs=['yref', 'vref'],
# System outputs
outlist=['vehicle.x', 'vehicle.y', 'vehicle.theta', 'controller.v',
'controller.phi'],
outputs=['x', 'y', 'theta', 'v', 'phi']
)
# Set up the simulation conditions
yref = 1
T = np.linspace(0, 5, 100)
# Set up a figure for plotting the results
mpl.figure();
# Plot the reference trajectory for the y position
mpl.plot([0, 5], [yref, yref], 'k--')
# Find the signals we want to plot
y_index = steering.find_output('y')
v_index = steering.find_output('v')
# Do an iteration through different speeds
for vref in [8, 10, 12]:
# Simulate the closed loop controller response
tout, yout = ct.input_output_response(
steering, T, [vref * np.ones(len(T)), yref * np.ones(len(T))])
# Plot the reference speed
mpl.plot([0, 5], [vref, vref], 'k--')
# Plot the system output
y_line, = mpl.plot(tout, yout[y_index, :], 'r') # lateral position
v_line, = mpl.plot(tout, yout[v_index, :], 'b') # vehicle velocity
# Add axis labels
mpl.xlabel('Time (s)')
mpl.ylabel('x vel (m/s), y pos (m)')
mpl.legend((v_line, y_line), ('v', 'y'), loc='center right', frameon=False)
|