Abstract:

For a class of timevarying nonlinear systems with nonperiodically parameterized uncertainties, a new iterative learning neural network approximator (ILNNA) is designed to solve the design difficulties brought by the nonperiodical timevarying uncertainties. Subsequently, the ILNNA is directly applied to approximating the desired controller. At the same time, the controller is designed by the adaptive iterative learning control technique. Corresponding stability theorem is obtained according to Lyapunov stability theory. Then the conclusion that all state variables are bounded and the output will converge to a neighborhood of the desired trajectory can be obtained. Simulation results verify the correctness of the proposed scheme.