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This paper presents neural networks iterative learning control for a class of nonlinear time-varying systems. A finite time boundary layer is introduced and the inherent property of terminal sliding modes is exploited to realize finite time convergence, in the presence of initial repositioning errors. The neural networks employed in the controls have time-varying weights. Both indirect and direct...
This paper presents a learning control method for time-varying uncertain systems, whose uncertainties are bounded by a parameterized function and the unknowns are assumed to be time-varying. A deadzone-modified iterative update mechanism to handle initial condition errors is incorporated in the control design, without assuming that the bound on initial condition errors is small enough in consideration...
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