Multi-constrained model predictive control for autonomous ground vehicle trajectory tracking

A multi-constrained model predictive control (MPC) algorithm for trajectory tracking of an autonomous ground vehicle is proposed and tested in this paper. First, to simplify the computation, an active steering linear error model is applied in the MPC controller. Then, a control increment constraint and a relaxing factor are taken into account in the objective function to ensure the smoothness of the trajectory, using a softening constraints technique. In addition, the controller can obtain optimal control sequences which satisfy both the actual kinematic constraints and the actuator constraints. The circular trajectory tracking performance of the proposed method is compared with that of another MPC controller. To verify the trajectory tracking capabilities of the designed controller at different desired speed, the simulation experiments are carried out at the speed of 3m/s, 5m/s and 10m/s. The results demonstrate the MPC controller has a good speed adaptability.