Robust Adaptive Iterative Learning Control with Switching σ$$ \sigma $$‐Modification

ABSTRACT In this paper, the problem of robust adaptive iterative learning control (RAILC) with switching ‐modifications is addressed for a class of nonlinear systems with unrepeatable uncertainties and initial errors. Different from the published learning algorithms, switching learning is introduced to ensure the robustness of the parametric estimation. The causal contradiction caused by the switching ‐modification is solved by applying an open‐loop learning law. Sufficient conditions for initial rectifying functions (IRFs) are given for constructing prespecified tracking error trajectories, which are adopted in RAILC algorithms to cope with initial errors. S‐class function with a series convergence sequence is utilized in the controller design to guarantee the perfect tracking performance. Simulations are given to compare the switching‐ and the saturated learning that demonstrate the effectiveness of the proposed learning control scheme.