MPC and PSO Based Control Methodology for Path Tracking of 4WS4WD Vehicles

Abstract

Four wheel steering and four wheel drive (4WS4WD) vehicles are over-actuated systems with superior performance. Considering the control problem caused by the system nonlinearity and over-actuated characteristics of the 4WS4WD vehicle, this paper presents two methods to enable a 4WS4WD vehicle to accurately follow a predefined path as well as its reference trajectories including velocity and acceleration profiles. The methodologies are based on model predictive control (MPC) and particle swarm optimization (PSO), respectively. The MPC method generates the virtual inputs in the upper controller and then allocates the actual inputs in the lower controller using sequential quadratic programming (SQP), whereas the PSO method is proposed as a fully optimization based method for comparison. Both methods achieve optimization of the steering angles and wheel forces for each of four independent wheels simultaneously in real time. Simulation results achieved by two different controllers in following the reference path with varying disturbances are presented. Discussion about two methodologies is provided based on their theoretical analysis and simulation results.

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

“…It minimizes the gap between the reference path and the actual path by the vehicle dynamics model in a prediction horizon, and it has become a popular method in the control of autonomous vehicle. For the 4WS4WD vehicle path tracking control, Qifan Tan et al proposed a force-driven control method based on the combination of MPC and sequential quadratic programming using cascade control framework [40][41]. Chuanyang Sun et al studied the path tracking for autonomous vehicle based MPC and believed that path tracking accuracy and vehicle stability can hardly be accomplished by one fixed control frame in various conditions.…”

A Model Predictive Controller with Longitudinal Speed Compensation for Autonomous Vehicle Path Tracking

“…To prove the asymptotic stability of closed-loop discrete system formed by Equation 8, we assume the final target can be achieved, which is the error between the final predicted output and reference states become zero. The Lyapunov function V(x k ) is defined by the objective function formed by Equation (12) at the sample time k [38,39], which can be expressed as…”

A Trajectory Tracking Control Strategy of 4WIS/4WID Electric Vehicle with Adaptation of Driving Conditions

“…Y are the longitudinal and lateral velocity in the global coordinate system; X and Y are the position coordinates in the global coordinate system; and ϕ is the vehicle heading angle. Combining the first two Equations in Reference (22), the constraint equation can be obtained as follows:…”

“…In Reference [21], the lateral displacement at the virtual points near front and rear axles as new state variables is used in the path tracking controller design. Tan et al presents two methods to enable a 4WIS 4WID vehicle to accurately follow a predefined path as well as its reference trajectories including velocity and acceleration profiles [22]. In Reference [23], the projected error e p combine the lateral position error e and the heading angle error ∆φ is used to design the lateral controller.…”