Sampled-data iterative learning control for nonlinear systems with arbitrary relative degree

In this paper, a sampled-data iterative learning control method is proposed for nonlinear systems without restriction on system relative degree. The l earning algorithm does not require numerical differentiations of any order from the tracking error. A sufficient condition is derived to guarantee the convergence of the system output at each sampling instant to the desired t rajectory. Numerical simulation is conducted to demonstrate the theoretical result. (C) 2000 Elsevier Science Ltd. All rights reserved.