Regional pole placement-based robust lateral controller for autonomous ground vehicles considering uncertainty

Abstract

Autonomous ground vehicles (AGVs) often face challenges in maintaining tracking accuracy and stability due to uncertainties and external factors, such as variations in road surface friction and wind. These factors, particularly at higher speeds, significantly hinder the ability to achieve the desired stability and tracking performance. To address these challenges, we propose a novel robust lateral controller (RLC) by exploiting the $H_{\infty }$ synthesis technique, considering uncertain cornering stiffness, with linear matrix inequality (LMI)-based regional pole placement constraints (RPPC). The proposed regional pole placement constraints-based robust lateral controller (RPPC-RLC) with uncertainty is designed to be robust against variations in road conditions and external disturbances, ensuring the desired path-tracking accuracy and vehicle stability. A state-feedback control law is employed using a nonlinear vehicle dynamics model to develop LMIs as performance conditions. Additionally, we utilised RPPC technique to precisely refine the controller gain for ensuring precise stability and robust performance in the presence of uncertainties and active disturbances. The proposed controller’s effectiveness is rigorously examined under different road conditions, various AGV speeds, and both the presence and absence of wind disturbances, while cornering stiffness was considered an uncertain parameter. The performance of the controller was also compared with several commonly used controllers, such as the model predictive controller (MPC), $H_2$, conventional robust controller, and the Linear Quadratic Regulator (LQR). The results demonstrate that the proposed controller outperforms these alternatives in terms of minimising the lateral position error and heading error, based on different statistical parameters. Furthermore, we validated the controller’s performance in a MATLAB/Simulink environment using a 14-degree-of-freedom complex vehicle model. Finally, the proposed RPPC-RLC with uncertainty exhibited efficient tracking performance and maintained the stability of the AGV under varying road conditions and wind disturbances at different speeds, even at high speeds.