A Novel Adaptive NN Prescribed Performance Control for Stochastic Nonlinear Systems

This article investigates the problem of neural network (NN)-based adaptive backstepping control design for stochastic nonlinear systems with unmodeled dynamics in finite-time prescribed performance. NNs are used to study the uncertain control plants, and the problem of unmodeled dynamics is tackled...

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Bibliographic Details
Published inIEEE transaction on neural networks and learning systems Vol. 32; no. 7; pp. 3196 - 3205
Main Authors Sui, Shuai, Chen, C. L. Philip, Tong, Shaocheng
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.07.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive control
Adaptive systems
Artificial neural networks
Backstepping
Computer simulation
Control design
Control systems
Control systems design
Design
Design modifications
Dynamical systems
Finite-time performance function (FTPF)
Mathematical models
Neural networks
Nonlinear control
Nonlinear systems
prescribed performance
Stochastic processes
Stochastic systems
Stochasticity
Tracking control
Tracking errors
unmodeled dynamics
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Summary:This article investigates the problem of neural network (NN)-based adaptive backstepping control design for stochastic nonlinear systems with unmodeled dynamics in finite-time prescribed performance. NNs are used to study the uncertain control plants, and the problem of unmodeled dynamics is tackled by the combination of the changing supply function and the dynamical signal function methods. The outstanding contribution of this article is that based on the finite-time performance function (FTPF), a modified finite-time adaptive NN control design strategy is proposed, which makes the controller design simpler. Eventually, by using the Itô 's differential lemma, the backstepping recursive design technique, and the FTPFs, a novel adaptive prescribed performance tracking control scheme is presented, which can guarantee that all the variables in the control system are bounded in probability, and the tracking error can converge to a specified performance range in the finite time. Finally, both numerical simulation and applied simulation examples are provided to verify the effectiveness and applicability of the proposed method.
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ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2020.3010333