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Design method of adaptive nonlinear H control systems via neural network approximators

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1 Author(s)
Miyasato, Y. ; Inst. of Stat. Math., Tokyo, Japan

A class of adaptive nonlinear H control systems for nonlinear and time-varying processes which include nonlinear parametric models approximated by multi-layer neural networks, is proposed. Those control schemes are derived as solutions of particular nonlinear H control problems, where unknown system parameters and approximate and algorithmic errors in neural networks are regarded as exogenous disturbances to the processes, and thus, in the resulting control systems, the L2 gains from those virtual disturbances to generalized outputs are made less than the prescribed positive constants γ(> 0). The proposed control systems are shown to be bounded for arbitrarily large but bounded variations of time-varying parameters and approximate and algorithmic errors in neural network approximators

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Decision and Control, 2001. Proceedings of the 40th IEEE Conference on  (Volume:4 )

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