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

In this paper, a sampled-daata iterative learning control method is proposed for nonlinear systems without restriction on system relative degree. The learning 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 trajectory. Numerical simulation is conducted to demonstrate the theoretical result.

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