Differentiation-Free Multiswitching Neuroadaptive Control of Strict-Feedback Systems

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Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/41905

Title:	Differentiation-Free Multiswitching Neuroadaptive Control of Strict-Feedback Systems
Authors:	Huang, JT (Huang, Jeng-Tze) Pham, TP (Thanh-Phong Pham)
Contributors:	機械工程系暨機械工程學系數位機電研究所
Keywords:	DYNAMIC SURFACE CONTROL ADAPTIVE NEURAL-CONTROL MIMO NONLINEAR-SYSTEMS OUTPUT-FEEDBACK BACKSTEPPING CONTROL TRACKING CONTROL NETWORK CONTROL A-PRIORI STABILIZATION STABILITY
Date:	2018-04
Issue Date:	2019-01-21 13:35:38 (UTC+8)
Abstract:	Issues of differentiation-free multiswitching neuroadaptive tracking control of strict-feedback systems are presented. It mainly consists of a set of nominal adaptive neural network compensators plus an auxiliary switched linear controller that ensures the semiglobally/globally ultimately uniformly bounded stability when the unknown nonlinearities are locally/globally linearly bounded, respectively. In particular, the so-called explosion of complexity is annihilated in two steps. First, a set of first-order low-pass filters are constructed for solving such a problem in the nominal neural compensators. In contrast to most existing dynamic surface control-based schemes, bounded stability of the filter dynamics is ensured by virtue of the localness and hence boundedness of the neural compensators. Separation of controller-filter pairs is thus achieved in this paper. Next, an auxiliary switched linear state feedback control is synthesized to further solve such a problem in the nonneural regions. Besides being differentiation-free, such an approach provides more flexibility for meeting various control objectives at a time. An earlier proposed smooth switching algorithm is also incorporated to tackle the control singularity problem. Finally, simulation works are presented to demonstrate the validity of the proposed scheme.
Appears in Collections:	[Department of Mechanical Engineering ] conference paper

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