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Neural-Networks-Based Prescribed Tracking for Nonaffine Switched Nonlinear Time-Delay Systems.

Neural-Networks-Based Prescribed Tracking for Nonaffine Switched Nonlinear Time-Delay Systems. In this article, by using the neural-networks (NNs) separation and approximation technique, an adaptive scheme is presented to deliver the prescribed tracking performance for a class of unknown nonaffine switched nonlinear time-delay systems. The nonaffine terms are indifferentiable and the controllability condition is not required for each subsystem, which allows the considered tracking problem to not be efficiently solved by the traditional adaptive control algorithms. To solve the problem, NNs are utilized to separate and approximate the nonaffine functions, and then the dynamic surface control and convex combination method are utilized to construct a controller and a switching strategy. In addition, an adaptive law is considered for each subsystem to reduce the conservativeness. Under the designed controller and switching strategy, all the signals of the resulting closed-loop system are bounded, and the tracking performance is achieved with a prescribed level. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png IEEE Transactions on Cybernetics Pubmed

Neural-Networks-Based Prescribed Tracking for Nonaffine Switched Nonlinear Time-Delay Systems.

IEEE Transactions on Cybernetics , Volume 52 (7): 12 – Jul 6, 2022

Neural-Networks-Based Prescribed Tracking for Nonaffine Switched Nonlinear Time-Delay Systems.


Abstract

In this article, by using the neural-networks (NNs) separation and approximation technique, an adaptive scheme is presented to deliver the prescribed tracking performance for a class of unknown nonaffine switched nonlinear time-delay systems. The nonaffine terms are indifferentiable and the controllability condition is not required for each subsystem, which allows the considered tracking problem to not be efficiently solved by the traditional adaptive control algorithms. To solve the problem, NNs are utilized to separate and approximate the nonaffine functions, and then the dynamic surface control and convex combination method are utilized to construct a controller and a switching strategy. In addition, an adaptive law is considered for each subsystem to reduce the conservativeness. Under the designed controller and switching strategy, all the signals of the resulting closed-loop system are bounded, and the tracking performance is achieved with a prescribed level.

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ISSN
2168-2267
eISSN
2168-2275
DOI
10.1109/TCYB.2020.3042232
pmid
33417582

Abstract

In this article, by using the neural-networks (NNs) separation and approximation technique, an adaptive scheme is presented to deliver the prescribed tracking performance for a class of unknown nonaffine switched nonlinear time-delay systems. The nonaffine terms are indifferentiable and the controllability condition is not required for each subsystem, which allows the considered tracking problem to not be efficiently solved by the traditional adaptive control algorithms. To solve the problem, NNs are utilized to separate and approximate the nonaffine functions, and then the dynamic surface control and convex combination method are utilized to construct a controller and a switching strategy. In addition, an adaptive law is considered for each subsystem to reduce the conservativeness. Under the designed controller and switching strategy, all the signals of the resulting closed-loop system are bounded, and the tracking performance is achieved with a prescribed level.

Journal

IEEE Transactions on CyberneticsPubmed

Published: Jul 6, 2022

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