Trajectory Tracking Control of a Four-wheel Mobile Robot Based on Adaptive Sliding Mode Control
摘要
This study aimed to address the estimation and compensation of unmatched uncertainties with unknown bounds in sliding mode control. A non-singular terminal sliding mode controller was designed for a four-wheel mobile robot. To ensure the existence of the sliding surface, Gaussian process regression (GPR) was employed for online estimation of the unmatched uncertainties. GPR not only estimated the bound of the uncertainties but also provided the mean and variance, which allows for a more robust estimation. On the one hand, the use of GPR for uncertainty estimation could help avoid the use of high-gain control, thereby reducing control chattering. On the other hand, the uncertainty compensation based on the estimates from GPR could enhance the adaptability of the modelbased sliding mode control algorithm. Additionally, an adaptive terminal sliding mode controller was designed based on the proximal policy optimization (PPO) algorithm. A reward function was constructed with the objective of improving control accuracy and minimizing control input chattering, which could enable the adaptive adjustment of the sliding mode controller’s parameters. The stability of the non-singular terminal sliding mode controller was proven through Lyapunov stability analysis. The effectiveness of the proposed control algorithm is validated through numerical simulations. The results demonstrated that the adaptive terminal sliding mode controller based on GPR significantly reduced by 90% while achieving high control accuracy, outperforming traditional control methods.