Convergence for SISO ILC Systems with Locally Lipschitz Nonlinear Dynamics

摘要

This paper is devoted to iterative learning control (ILC) for single-input single-output (SISO), affine nonlinear systems with locally Lipschitz dynamics and subject to iteration-varying uncertainties arising from external disturbances and initial state shifts. By adopting a P-type update law, a necessary and sufficient condition is proposed to ensure the convergence of nonlinear SISO ILC. It is shown that the ILC process converges robustly with the final error bound depending continuously upon the bounds of iteration-varying uncertainties. Simulations illustrate the validity of the convergence results.