A Tube Model Predictive Control Method for Autonomous Lateral Vehicle Control Based on Sliding Mode Control

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-04, Vol.23 (8), p.3844
• Main Authors: Dai, Yong, Wang, Duo
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analysis; autonomous vehicle (AV); Control algorithms; Control methods; Control systems; Control theory; Controllers; Design; Disturbances; Driverless cars; Kinematics; Methods; model predictive control (MPC); Motion control; path tracking; Roads & highways; Robust control; Sliding mode control; sliding mode control (SMC); tube MPC; Uncertainty; Vehicles
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23083844

This paper aims to enhance the lateral path tracking control of autonomous vehicles (AV) in the presence of external disturbances. While AV technology has made significant strides, real-world driving scenarios often pose challenges such as slippery or uneven roads, which can adversely affect the lateral path tracking control and reduce driving safety and efficiency. Conventional control algorithms struggle to address this issue due to their inability to account for unmodeled uncertainties and external disturbances. To tackle this problem, this paper proposes a novel algorithm that combines robust sliding mode control (SMC) and tube model predictive control (MPC). The proposed algorithm leverages the strengths of both MPC and SMC. Specifically, MPC is used to derive the control law for the nominal system to track the desired trajectory. The error system is then employed to minimize the difference between the actual state and the nominal state. Finally, the sliding surface and reaching law of SMC are utilized to derive an auxiliary tube SMC control law, which helps the actual system keep up with the nominal system and achieve robustness. Experimental results demonstrate that the proposed method outperforms conventional tube MPC, linear quadratic regulator (LQR) algorithms, and MPC in terms of robustness and tracking accuracy, especially in the presence of unmodeled uncertainties and external disturbances.