A robust lateral tracking control strategy for autonomous driving vehicles. (March 2021) - Zhang, Wei
Highlights: This paper is one of the first to take dynamics of steering system into controller design. This paper introduces a quadratic performance function and derives robust control law based on Lyapunov theory and gain scheduling method to keep both stability and performance robustness. Results show that the proposed control strategy can regulate vehicle to follow target trajectory precisely even when speed varies. Abstract: A robust steering torque control strategy for lateral tracking functionality of autonomous driving vehicles that perform active steering, active accelerating and active braking is researched in this paper. The main target of this research is to track references of lateral position and heading angle, which are provided by upstream motion planning module, via torque that robotic arm applies to steering hand wheel. Firstly, a system with tracking errors is generated and analyzed. To keep control system's robustness against time varying parameters, such as speed, center of mass, etc., gain-scheduling approach is utilized to obtain proper feedback gain. To achieve performance robustness, methods of linear matrix inequalities are used in design to maintain a performance function. Control performance is validated in Hardware in the loop environment built by ETAS labcar and Matlab/Simulink for a lane changing scenario and two typical parking scenarios. Results show that the proposed control strategy can regulate vehicle to follow target trajectory precisely even when speed varies. In lane changing scenario, steady state error is close to zero and maximum lateral position error is about ±30 cm. In parking scenarios, tracking error of lateral position is about ±3 cm, and of heading angle is about ±0.1 rad at end points. In addition, in comparison with linear quadratic regulator (LQR) and model predictive control (MPC), the proposed control outperformances in the three test scenarios.