2017
DOI: 10.1155/2017/2981518
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Constrained Adaptive Neural Control of Nonlinear Strict‐Feedback Systems with Input Dead‐Zone

Abstract: This paper focuses on a single neural network tracking control for a class of nonlinear strict-feedback systems with input dead-zone and time-varying output constraint via prescribed performance method. To release the limit condition on previous performance function that the initial tracking error needs to be known, a new modified performance function is first constructed. Further, to reduce the computational burden of traditional neural back-stepping control approaches which require all the virtual controller… Show more

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Cited by 1 publication
(1 citation statement)
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“…In the past several decades, approximation-based adaptive control of nonlinear systems has been attracting much attention, and many significant results have been achieved [1][2][3][4][5][6][7][8][9][10][11]. Among them, the fuzzy logic systems (FLSs) and neural networks (NNs) have been successfully employed to approximate the unknown nonlinear functions.…”
Section: Introductionmentioning
confidence: 99%
“…In the past several decades, approximation-based adaptive control of nonlinear systems has been attracting much attention, and many significant results have been achieved [1][2][3][4][5][6][7][8][9][10][11]. Among them, the fuzzy logic systems (FLSs) and neural networks (NNs) have been successfully employed to approximate the unknown nonlinear functions.…”
Section: Introductionmentioning
confidence: 99%