Iterative Learning‐Based Sliding Mode Control for Four‐Mecanum‐Wheel Mobile Robots

The trajectory tracking control of four‐Mecanum‐wheel mobile robots (FMWMRs) is challenged by high requirements for safety and precision amid complex operating conditions. Existing automatic control methods often ignore valuable historical data and the influence of practical motor torque, thereby complicating engineering implementation and impeding control performance optimization. This paper proposes a novel iterative learning‐based sliding mode control scheme for FMWMRs with input saturation, unknown uncertainties, and external disturbances. This scheme integrates the error self‐correction capability of iterative learning control with the strong robustness of fast nonsingular terminal sliding mode control (TSMC). The comprehensive robustness of the system is effectively enhanced, and the asymptotic stability of the proposed controller is rigorously established using composite energy function methods and Lyapunov theory. Furthermore, a smoothing function is employed to address the nonlinear effects of the input saturation, thereby facilitating a more straightforward and practical controller design. Simulation results demonstrate that the proposed scheme achieves superior trajectory tracking accuracy, faster error convergence, and stronger robustness than conventional approaches, making it a promising solution for high‐precision FMWMR applications in industrial environments.