Validation of Personalized Control Algorithms for Intelligent Vehicles Based on Hardware-in-the-Loop Testing

With the continuous development of intelligent vehicle control technology, it has become a critical challenge to comprehensively test both controllers and control algorithms under realistic conditions. Considering safety, feasibility, and cost-effectiveness, the construction of a hardware-in-the-loop (HIL) testing system is an essential requirement for the automotive control industry. In this paper, we design and construct a HIL testing system to verify and analyze the reliability and safety of the proposed personalized control algorithm. The system is a hybrid configuration consisting of real components and virtual subsystems that work in concert to form a closed-loop, semi-virtual, and semi-real environment. Based on the principles of HIL systems and the architecture of autonomous driving systems, a HIL simulation platform for personalized control of intelligent vehicles is designed. Utilizing the established HIL testing system and the Speedgoat rapid control prototyping platform, the model predictive control (MPC) algorithm is executed in real time to verify the reliability of the personalized path tracking control algorithm under HIL conditions. The proposed HIL simulation platform provides theoretical and technical support for the development of intelligent driving control algorithms, hardware, and testing scenarios within the enterprise.