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Adaptive RBF Neural Network Control for Nonlinear System

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Complex Systems: Spanning Control and Computational Cybernetics: Foundations

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 414))

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Abstract

This chapter focuses on the neural network(NN)-based adaptive tracking control issue for a class of high-order nonlinear systems both subjected to the immeasurable state variables and unknown external disturbance as well as the actuator failures. Combining with the radial basis function neural networks(RBF NNs), the composite disturbance observer and state observer are established, respectively. Besides that, so as to c at ope with the sparsity of the control resources, the event-triggered control strategy is applied. The purpose of this work is to develop neural network-based adaptive tracking control schemes such that the output of nonlinear systems ultimately tracks that of the desired target and all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded by utilizing the back-step** technique. Finally, the numerical example is performed to verify the efficacy of the proposed approach.

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Author information

Authors and Affiliations

  1. College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, People’s Republic of China

    **n Wang

  2. T & M University, Doha, 23874, Qatar

    Tingwen Huang

  3. Techno India NJR Institute of Technology, Udaipur, 313003, Rajasthan, India

    Prasun Chakrabarti

Authors
  1. **n Wang
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  2. Tingwen Huang
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  3. Prasun Chakrabarti
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Corresponding author

Correspondence to **n Wang .

Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia

    Peng Shi

  2. Control and Informatics Division, JP Hydro-System “Streževo”, Bitola, North Macedonia

    Jovan Stefanovski

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Wang, X., Huang, T., Chakrabarti, P. (2022). Adaptive RBF Neural Network Control for Nonlinear System. In: Shi, P., Stefanovski, J., Kacprzyk, J. (eds) Complex Systems: Spanning Control and Computational Cybernetics: Foundations. Studies in Systems, Decision and Control, vol 414. Springer, Cham. https://doi.org/10.1007/978-3-030-99776-2_22

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