Iteration Learning Control for Uncertain Nonlinear Systems with Time-Varying Output Constraint

A novel Adaptive Iteration Learning Control (AILC) method is proposed to solve the trajectory tracking problem for a class of nonlinear uncertain systems with external disturbances. Furthermore, the output of system is required to be bounded by a time-varying function. To this end, a Barrier Lyapunov Function (BLF) term is integrated into the AILC scheme such that the impact of the uncertainties and disturbances are significantly reduced without violating the output constraints. A Barrier Composite Energy Function (BCEF) is utilized to analyze the convergence of state error and the boundedness of output. The validity of the proposed AILC scheme is verified by a numerical example. In addition, a high-fidelity simulation platform that can generate a real-life turbulent flow is utilized to demonstrate the robustness of the algorithm. Copyright (c) 2023 The Authors.