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Adaptive neural control of pure-feedback nonlinear time-delay systems via dynamic surface technique

Wang, Min and Liu, Xiaoping and Shi, Peng (2011) Adaptive neural control of pure-feedback nonlinear time-delay systems via dynamic surface technique. IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics, 41 (6). pp. 1681-1692. ISSN 1083-4419

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Abstract

This paper is concerned with robust stabilization problem for a class of nonaffine pure-feedback systems with unknown time-delay functions and perturbed uncertainties. Novel continuous packaged functions are introduced in advance to remove unknown nonlinear terms deduced from perturbed uncertainties and unknown time-delay functions, which avoids the functions with control law to be approximated by radial basis function (RBF) neural networks. This technique combining implicit function and mean value theorems overcomes the difficulty in controlling the nonaffine pure-feedback systems. Dynamic surface control (DSC) is used to avoid “the explosion of complexity” in the backstepping design. Design difficulties from unknown time-delay functions are overcome using the function separation technique, the Lyapunov–Krasovskii functionals, and the desirable property of hyperbolic tangent functions. RBF neural networks are employed to approximate desired virtual controls and desired practical control. Under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced significantly, and semiglobal uniform ultimate boundedness of all of the signals in the closed-loop system is guaranteed. Simulation studies are given to demonstrate the effectiveness of the proposed design scheme.

Item Type: Article
Uncontrolled Keywords: ResPubID24845, adaptive neural control, dynamic surface control, DSC, nonlinear time-delay systems, pure-feedback systems, artificial neural networks, backstepping, control systems, nonlinear systems
Subjects: FOR Classification > 0802 Computation Theory and Mathematics
FOR Classification > 0906 Electrical and Electronic Engineering
SEO Classification > 970108 Expanding Knowledge in the Information and Computing Sciences
Faculty/School/Research Centre/Department > Institute for Logistics and Supply Chain Management (ILSCM)
Depositing User: VUIR
Date Deposited: 07 Mar 2013 03:07
Last Modified: 07 Mar 2013 03:07
URI: http://vuir.vu.edu.au/id/eprint/10347
DOI: 10.1109/TSMCB.2011.2159111
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Citations in Scopus: 15 - View on Scopus

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