MPC-based path tracking with PID speed control for autonomous vehicles
Shuping Chen, Huiyan Chen
Abstract
Abstract In this paper, a new coupled lateral and longitudinal controller based on model predictive control (MPC) framework was proposed for an autonomous vehicle to track the desired trajectory and speed. Considering the constraints of control input limit and state output admissible, we used a spatial-based 8 degrees of freedom (DOF) vehicle model as the prediction model and used a high-fidelity model, i.e., a 14-DOF vehicle model as the plant model in the formulation of MPC algorithm. For the lateral control, the MPC controller generates the optimal road-wheel steering angle; for the longitudinal control, the PID controller embedded in the optimization solution generates the total driving or braking wheel torque. All these control inputs were passed to the plant simultaneously. The developed vehicle models were simulated with step steering input and compared with the simulation result of CarSim vehicle model for validation. We implemented the proposed controller for path tracking and speed control with MATLAB considering an 8-shaped curved trajectory as the reference. The simulation results showed that the path tracking and speed tracking performance were good using the combined lateral and longitudinal control strategy.