Autonomous Vehicle Steering Using Model Predictive Control
This example shows how to implement an autonomous vehicle steering system using model predictive control (MPC).
Contents
Add example file folder to MATLAB® path.
addpath(fullfile(matlabroot,'examples','mpc_featured','main'));
Lateral Vehicle Dynamics
To describe the lateral vehicle dynamics, this example uses a bicycle model with two degrees of freedom, lateral position and yaw angle. The vehicle model is depicted in the following figure.
Here,
- and denote the position of the vehicle in global coordinates.
- and denote the longitudinal and lateral velocities of the vehicle in body-fixed coordinates with respect to the center of gravity .
- is the yaw angle of the vehicle.
- is the front steering angle.
- is the reference lateral position.
- is the reference yaw angle.
The following vehicle parameter values are used in this example:
- m is the total vehicle mass (kg)
- Iz is the yaw moment of inertia of the vehicle (mNs^2).
- lf is the longitudinal distance from the center of gravity to the front tires (m).
- lr is the longitudinal distance from center of gravity to the rear tires (m).
- Cf is the cornering stiffness of the front tires (N/rad).
- Cr is the cornering stiffness of the rear tires (N/rad).
m = 1575; Iz = 2875; lf = 1.2; lr = 1.6; Cf = 19000; Cr = 33000;
In this example, the longitudinal vehicle dynamics are separated from the lateral vehicle dynamics. Therefore, the longitudinal velocity is assumed to be constant. You can represent the lateral vehicle dynamics using a linear time-invariant (LTI) system with the following state, input, and output variables.
- State variables: Lateral velocity , yaw angle , yaw angle rate , global position
- Input variable: Front steering angle
- Output variable: Global position and yaw angle
Specify the longitudinal velocity in m/s.
Vx = 15;
Specify a state-space model, vehicle, of the lateral vehicle dynamics.
A = [-(2*Cf+2*Cr)/m/Vx, 0, -Vx-(2*Cf*lf-2*Cr*lr)/m/Vx, 0; 0, 0, 1, 0; -(2*Cf*lf-2*Cr*lr)/Iz/Vx, 0, -(2*Cf*lf^2+2*Cr*lr^2)/Iz/Vx, 0; 1, Vx, 0, 0]; B = [2*Cf/m 0 2*Cf*lf/Iz 0]'; C = [0 0 0 1; 0 1 0 0]; vehicle = ss(A,B,C,0);
This LTI model approximates the global position of the vehicle using , which applies under small angle assumptions.
Reference Trajectory: Double Lane Change Maneuver
The reference trajectory to be followed is generated for a double lane change maneuver scenario. Given the longitudinal velocity of the vehicle, , define a reference trajectory for the vehicle using and . This scenario represents an emergency obstacle avoidance maneuver with a given longitudinal velocity. In this example, the obstacles are static and the reference trajectory that avoids the obstacles is predesigned.
Plot the reference path and obstacles.
T = 15; % simulation duration time = 0:0.1:T; % simulation time plotReference(Vx,time);
Design Model Predictive Controller with Previewing
In this example, the control objective is to avoid the obstacles by following the reference trajectory closely. The autonomous steering system is implemented using a linear MPC controller with look-ahead (previewing). This controller is similar to a driver steering model where the driver can look ahead and preview the path in front of the vehicle. With previewing, the controller takes actions in advance and thus improve tracking performance. In this example, the previewed reference trajectory contains two signals, position and yaw angle . The preview horizon is one second.
Open Simulink model.
mdl = 'mpcVehicleSteering';
open_system(mdl)
To design a model predictive controller for the plant model, first create an MPC controller using a default prediction horizon (10 steps), default control horizon (2 moves), and sample time Ts. The preview horizon is the product of the sample time and the prediction horizon.
Ts = 0.1; mpc1 = mpc(vehicle,Ts);
-->The "PredictionHorizon" property of "mpc" object is empty. Trying PredictionHorizon = 10. -->The "ControlHorizon" property of the "mpc" object is empty. Assuming 2. -->The "Weights.ManipulatedVariables" property of "mpc" object is empty. Assuming default 0.00000. -->The "Weights.ManipulatedVariablesRate" property of "mpc" object is empty. Assuming default 0.10000. -->The "Weights.OutputVariables" property of "mpc" object is empty. Assuming default 1.00000. for output(s) y1 and zero weight for output(s) y2
For this controller, there is one manipulated variable (front steering angle ) and there are two output variables ( position and yaw angle ).
To ensure driver comfort, constrain the rate of change of the steering angle to 0.26 rad/s (approximately 15 deg/s).
mpc1.MV.RateMin = -0.26; mpc1.MV.RateMax = 0.26;
To ensure tracking performance for both position and yaw angle , set both output tuning weights to be nonzero. Since the controller has one manipulated variable and two outputs, the system is underactuated. With nonzero weights for both outputs, perfect tracking is not expected. However, you can still guarantee satisfactory tracking performance by selecting appropriate weights.
mpc1.Weights.OV = [1 0.1];
Simulation Analysis
Run the simulation with the designed model predictive controller. The simulation data is logged in the logsout data set.
sim(mdl)
-->Converting model to discrete time. Assuming no disturbance added to measured output channel #1. -->Assuming output disturbance added to measured output channel #2 is integrated white noise. -->The "Model.Noise" property of the "mpc" object is empty. Assuming white noise on each measured output channel.
Plot the simulation result.
plotSimulation(logsout,Vx,time)
The vehicle trajectory follows the reference trajectory closely, thus avoiding the obstacles. When the vehicle travels along a straight line, that is when , both and are tracked perfectly. This result is due to the relations and . In other words, if either or is satisfied, then the other is satisfied as well.
Remove example file folder from MATLAB path, and close Simulink model.
rmpath(fullfile(matlabroot,'examples','mpc_featured','main')); bdclose(mdl)