A Simple Approach to Iterative Learning Control of Constrained High‐Order Nonlinear Systems

This paper proposes a conditional length strategy of iterative learning control to treat the full state constraints of high‐order nonlinear systems with a simple manner. Compared with the Barrier Lyapunov Function‐based method, the proposed manner features by avoiding sharp increases in input signal and complex virtual controller design processes. An actual controller is constructed via the backstepping technique without the inverse dynamic of the input matrix. Convergence of the tracking error for the complete interval is guaranteed by a virtual variables‐based composite energy function. Finally, the validity of the proposed method is verified through a simulation example.