Adaptive robust iterative learning control with dead zone scheme

Abstract

An adaptive robust iterative learning control method based on a new dead-zone scheme is presented for the control of nonlinear uncertain systems. The new dead-zone scheme ceases both learning and adaptation whenever the previous iteration error enters a pre-specified error bound, in the sequel enhances the robustness of the control system and meanwhile achieves arbitrary tracking accuracy. For guaranteed stability, it is indicated that the system will converge to the error bound within finite iterations and stay inside it, and that the boundedness of the system signals for any iteration is ensured (no finite escape time). Iterative learning control (ILC) and adaptive robust control are synthesized to achieve a new control paradigm. The iterative learning control strategy is applied directly to deal with structured system uncertainties — unknown time functions which are invariant over iterations. The adaptive robust control strategy is used to handle non-periodic system uncertainties associated with partially known bounding functions, where the unknown parameters in the upper bounding functions are estimated with adaptation. By integrating learning, adaptation, robust control and the new dead-zone scheme using Lyapunov's direct method, the proposed scheme is able to handle fairly broad classes of nonlinear uncertain systems.