Gain-Scheduled Sliding-Mode-Type Iterative Learning Control Design for Mechanical Systems

In this paper, a novel gain-scheduled sliding-mode-type (SM-type) iterative learning (IL) control approach is proposed for the high-precision trajectory tracking of mechanical systems subject to model uncertainties and disturbances. Based on SM variable, controller synthesized involving feedback regulation item, feedforward robust switching item. The item adopted regulate position velocity errors, applied handle repetitive disturbance, introduced compensate nonrepetitive disturbance linearization residual error. Moreover, mechanism employed both enhance convergence speed. Convergence analysis illustrates that errors can eventually zero under controller. By combining advantages IL control, has strong robustness against Lastly, simulations comparisons are provided evaluate efficiency excellent performance approach.