2020
DOI: 10.1002/rnc.5001
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Filtering adaptive neural network controller for multivariable nonlinear systems with mismatched uncertainties

Abstract: This paper synthesizes a filtering adaptive neural network controller for multivariable nonlinear systems with mismatched uncertainties. The multivariable nonlinear systems under consideration have both matched and mismatched uncertainties, which satisfy the semiglobal Lipschitz condition. The nonlinear uncertainties are approximated by a Gaussian radial basis function (GRBF)-based neural network incorporated with a piecewise constant adaptive law, where the adaptive law will generate adaptive parameters by so… Show more

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Cited by 7 publications
(5 citation statements)
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References 45 publications
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“…As a valid method in dealing with unknown uncertainties, [1][2][3] adaptive backstepping control has found wide applications in nonlinear control systems in the past few years. [4][5][6][7] To better deal with unknown uncertainties, some powerful methods are proposed, including neural network method [8][9][10] and fuzzy logic method. 11,12 Adaptive neural networks (ANNs)/fuzzy logics controllers are constructed to stabilize the considered nonlinear systems.…”
Section: Introductionmentioning
confidence: 99%
“…As a valid method in dealing with unknown uncertainties, [1][2][3] adaptive backstepping control has found wide applications in nonlinear control systems in the past few years. [4][5][6][7] To better deal with unknown uncertainties, some powerful methods are proposed, including neural network method [8][9][10] and fuzzy logic method. 11,12 Adaptive neural networks (ANNs)/fuzzy logics controllers are constructed to stabilize the considered nonlinear systems.…”
Section: Introductionmentioning
confidence: 99%
“…In [56], the authors proposed a trajectory tracking controller for a class of uncertain non-linear systems with time delay by using command filtered-based event-triggered adaptive neural network control. A filtering adaptive neural network-based controller with Gaussian radial basis function was developed in [57] to compensate mismatched uncertainties in multivariable non-linear systems. A backstepping-based controller with adaptive neural networks was proposed in [58] for non-strict-feedback systems with input delays.…”
Section: Introductionmentioning
confidence: 99%
“…Due to their unique features, designing a controller that be able to optimally and robustly manage nonlinear systems subjected to mismatched uncertainties has been the desire of researchers. In this respect, different control methods have been proposed to make nonlinear systems immune against mismatched uncertainties; such as robust control methods, 14‐20 adaptive control schemes, 21‐24 and hybrid control systems 25‐33 . In dealing with mismatched uncertainties, an appropriate controller should make an uncertain nonlinear system robust, while achieving a desired performance.…”
Section: Introductionmentioning
confidence: 99%
“…For managing multivariable nonlinear plants against mismatched uncertainties, a filtering adaptive neural network (NN) control strategy was devised in Reference 22. In that work, fast adaptation was achieved through the integration of Gaussian radial basis function and piecewise constant adaptive rule.…”
Section: Introductionmentioning
confidence: 99%