Iterative learning control for a class of nonlinear systems

The iterative learning control (ILC) is derived for a class of nonlinear systems and is proved to converge to the desired control with probability one, minimizing some performance index. The state equation is nonlinear and the observation equation is with noise. The control sequence does not directly feed to the system but first passes through a nonlinear function such as the dead-zone, pre-load, and saturation etc. Then the outputs of these nonlinearities linearly input to the system.

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