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Modular Self-Organizing Control for Linear and Nonlinear Systems

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Book cover Advances in Self-Organizing Maps and Learning Vector Quantization

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 428))

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Abstract

Nowadays, a good control system must meet some complex requirements. Two important ones are: quick and accurate responses to sudden changes in systems. This paper presents a control strategy for Self-Organizing Maps (SOM) that can do so. The proposed SOM-based control has a multiple-module architecture and learns from feedback on errors which enables it to generate appropriate controllers. Simulations of the mass-spring-damper system and the inverted pendulum validated the model. In the experiments, the systems had time-varying parameters. The results from the method proposed were compared with conventional methods and previous self-organizing control and suggest that the proposed control is suitable for controlling linear and nonlinear systems which undergo sudden changes.

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Acknowledgments

The authors would like to thank CNPq for supporting this research.

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Authors and Affiliations

  1. Center of Informatics, Federal University of Pernambuco (UFPE), Av. Jornalista Anibal Fernandes, Recife, CEP 50740-560, Brazil

    Paulo Henrique Muniz Ferreira & Aluízio Fausto Ribeiro Araújo

Authors
  1. Paulo Henrique Muniz Ferreira
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  2. Aluízio Fausto Ribeiro Araújo
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Corresponding author

Correspondence to Aluízio Fausto Ribeiro Araújo .

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Editors and Affiliations

  1. Department of Statistics, Rice University, Houston, Texas, USA

    Erzsébet Merényi

  2. Department of Electrical and Computer En, Air Force Institute of Technology, Wright-Patterson AFB, Ohio, USA

    Michael J. Mendenhall

  3. Applied Physics, Rice University, Houston, Texas, USA

    Patrick O'Driscoll

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© 2016 Springer International Publishing Switzerland

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Ferreira, P.H.M., Araújo, A.F.R. (2016). Modular Self-Organizing Control for Linear and Nonlinear Systems. In: Merényi, E., Mendenhall, M., O'Driscoll, P. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 428. Springer, Cham. https://doi.org/10.1007/978-3-319-28518-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-28518-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28517-7

  • Online ISBN: 978-3-319-28518-4

  • eBook Packages: EngineeringEngineering (R0)

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