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This paper presents a characteristic modeling method for continuous/discrete time-varying nonlinear systems, where the model, the first-order time-varying differential equation, is a unified one. Learning identification algorithms are suggested for the purpose of parameter estimation, and the adaptive iterative learning control strategy is proposed for achieving the perfect tracking of the desired...
This paper presents feedback-aided iterative learning control strategies for linear time-invariant systems. Sufficient conditions of convergence of the feedback-aided PD-type learning algorithm are derived, and the converged output trajectory is given. The initial rectifying action is applied to eliminate the effect of initial shifts. It is shown that the system output converges to the desired one...
This paper presents an iterative learning control scheme for linear systems in the presence of a fixed initial state shift between iterative initial state and the desired one. The finite-time control strategies are adopted in the design of iterative learning controllers, and feedback-aided strategies are applied as well. The sufficient conditions for convergence of the learning control algorithms...
This paper presents a piecewise-reaching variable structure repetitive control method for uncertain discrete-time linear system. As for the reaching law approach, one needs to modify the original reaching law, as the resultant error dynamics depend on the system uncertainties, By embedding the performance measure of uncertainty rejection into the reaching law to form the ideal switching dynamics,...
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...
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