A practical trajectory tracking control of autonomous vehicles using linear time-varying MPC method

With the rapid development and implementation of autonomous vehicles (AVs), the simultaneous and accurate trajectory tracking problem for such AVs has become a popular research topic. This paper proposes a comprehensive linear time-varying model predictive controller (LTV-MPC) design for a type of AV, aiming to achieve good trajectory tracking in a practical driving scenario. First, a two-degree-of-freedom kinematic model of an AV is established. Next, an error model of the AV's trajectory tracking system is constructed using linear time-varying theory. A successive linearization is introduced to linearize the nonlinear tracking error model, and a quadratic programming optimization problem is then formulated. Thus, the control sequence for this AV is incorporated into the predictive control framework, and the desired controller can be solved with a relatively higher computational efficiency and lower computational cost. Finally, the effectiveness and performance of the proposed controller are validated via a comparison of simulations conducted using MATLAB software and experiments conducted using a self-established test platform. The results demonstrate that the proposed LTV-MPC method can track the prescribed reference road trajectories with high precision and stability for an AV under various driving conditions.