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This paper presents a partial-period adaptive repetitive control method for a class of periodically time-varying nonlinear systems. To reduce the size of memory usage, the symmetric feature of periodic parameters is explored to form partial-period adaptation mechanisms. Both Half- and quarter-period adaptation strategies are proposed, and characterized analytically. The stability of the closed-loop...
This paper presents a neural network framework for implementing unknown time-varying mappings. A unified architecture of time-varying neural networks is proposed, and the methodology of iterative learning is used for the network training. Convergence results of the iterative learning least squares algorithm are derived under assumption of bounded input signals. Periodic neural networks are explored...
In this paper, the design of adaptive controllers is presented for a class of periodically time-varying nonlinear systems. Saturated adaptation mechanisms are suggested for providing bounded estimation, by which the designed controllers can drive the state error to zero, while all the signals in the closed-loop are ensured to be bounded. The flexibility in choice of adaptation laws is illustrated...
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