Repetitive Trajectory Tracking of Autonomous Mobile Robots via SMC with Iterative Learning

Aiming at the increasingly strict trajectory tracking accuracy requirements and the characteristics of repetitive tasks of autonomous mobile robots (AMR), this paper proposes a robust control strategy that combines iterative learning control (ILC) and adaptive sliding model control (ASMC) in parallel to handle unknown disturbances and model uncertainties. Specifically, ASMC is employed as the main controller to deal with the lumped disturbances/uncertainties, which ensures the stability and strong robustness of the system. A PD-type iterative learning controller is used as the auxiliary controller to further improve tracking performance in repetitive motion tasks. The experimental results show that the ASMC-ILC controller outperforms both the iterative learning control and sliding mode control algorithms in terms of control performance.