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• W2169525442 abstract "In this paper, the recently proposed neural network adaptive robust control (NNARC) design axe generalized to synthesize performance oriented control laws for a class of nonlinear systems transformable to the semi-strict feedback forms through the incorporation of backstepping design techniques. All unknown but repeatable nonlinearities in system are approximated by outputs of multi-layer neural networks to achieve a better model compensation and an improved performance. Through the use of discontinuous projections with fictitious bounds, a controlled on-line training of all NN weights is achieved. Robust control terms can then be constructed to attenuate various model uncertainties effectively for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy." @default.
• W2169525442 created "2016-06-24" @default.
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• W2169525442 date "2001-01-01" @default.
• W2169525442 modified "2023-09-26" @default.
• W2169525442 title "Neural network adaptive robust control of nonlinear systems in semi-strict feedback form" @default.
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• W2169525442 doi "https://doi.org/10.1109/acc.2001.946180" @default.
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