Model predictive path following control for autonomous cars considering a measurable disturbance: Implementation, testing, and verification

Model predictive control (MPC)-based path following schemes for autonomous cars represent a novel and highly debated control approach. Their high online computational load poses a challenge for practical real-time application in vehicle systems with fast dynamics. This paper proposes an implementation scheme for an MPC path following controller that considers the feasible road region and vehicle shape. Moreover, the model mismatch induced by varying road conditions and small-angle assumptions is considered in the form of a measurable disturbance. To solve the optimization problem for the proposed MPC path following controller, a differential evolution (DE) algorithm is adopted. To verify the computational performance of the proposed implementation scheme, an experimental platform was developed that consists of the Hongqi autonomous car HQ430, various sensors, and systems for communication and data processing. The experimental results indicate that the proposed DE-based implementation strategy for the MPC path following controller achieves good computational performance and satisfactory control performance for path following in autonomous cars.