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Globally stable adaptive robust tracking control using RBF neural networks as feedforward compensators

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Abstract

In previous adaptive neural network control schemes, neural networks are usually used as feedback compensators. So, only semi-globally uniformly ultimate boundedness of closed-loop systems can be guaranteed, and no methods are given to determine the neural network approximation domain. However, in this paper, it is showed that if neural networks are used as feedforward compensators instead of feedback ones, then we can ensure the globally uniformly ultimate boundedness of closed-loop systems and determine the neural network approximation domain via the bound of known reference signals. It should be pointed out that this domain is very important for designing the neural network structure, for example, it directly determines the choice of the centers of radial basis function neural networks. Simulation examples are given to illustrate the effectiveness of the proposed control approaches.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their comments that improve the quality of the paper. This work was supported by the National Natural Science Foundation of P. R. China (60804021, 61072106, 61072139, 61001202), the Fundamental Research Funds for the Central Universities (JY10000970001), and the China Postdoctoral Science Foundation funded project (20090461282).

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Authors and Affiliations

  1. Department of Applied Mathematics and the Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an, 710071, China

    Weisheng Chen

  2. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, Xi’an, 710071, China

    L. C. Jiao & Jianshe Wu

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  1. Weisheng Chen
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  2. L. C. Jiao
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  3. Jianshe Wu
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Correspondence to Weisheng Chen.

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Chen, W., Jiao, L.C. & Wu, J. Globally stable adaptive robust tracking control using RBF neural networks as feedforward compensators. Neural Comput & Applic 21, 351–363 (2012). https://doi.org/10.1007/s00521-010-0455-8

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  • DOI: https://doi.org/10.1007/s00521-010-0455-8

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