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Repetitive learning control for a class of nonlinear systems with non-parameterized uncertainties

This paper deals with the problem of repetitive learning control for a class of nonlinear systems with non-parametric uncertainties. The control direction of the system is unknown. In the previous studies on neural network control of uncertain systems, only is the semi-global and proximate control achieved if the control direction is unknown. In the paper, based on the technique of global approximation of unknown continuous functions by neural networks, a global repetitive learning control method is presented, which guarantees that the tracking error converges to zero on the repetitive interval uniformly.