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Model Predictive Control of Linear Parameter Varying Systems Based on a Recurrent Neural Network

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Book cover Theory and Practice of Natural Computing (TPNC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8890))

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Abstract

This paper presents a model predictive control approach to discrete-time linear parameter varying systems based on a recurrent neural network. The model predictive control problem is formulated as a sequential convex optimization, and it is solved by using a recurrent neural network in real time. The essence of the proposed approach lies in its real-time computational capability with extended applicability. Simulation results are provided to substantiate the effectiveness of the proposed model predictive control approach.

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Author information

Authors and Affiliations

  1. Huawei Shannon Lab, Shenzhen, China

    Zheng Yan

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

    Xinyi Le & Jun Wang

Authors
  1. Zheng Yan
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  2. Xinyi Le
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  3. Jun Wang
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Editor information

Editors and Affiliations

  1. Research Group on Mathematical Linguistics (GRLMC), Rovira i Virgili University, Avinguda Catalunya, 35, 43002, Tarragona, Spain

    Adrian-Horia Dediu  & Carlos Martín-Vide  & 

  2. E.T.S. de Ingenierías Informática y de Telecomunicación, DECSAI, University of Granada, C/Periodista Daniel Saucedo Aranda s/n, 18071, Granada, Spain

    Manuel Lozano

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© 2014 Springer International Publishing Switzerland

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Yan, Z., Le, X., Wang, J. (2014). Model Predictive Control of Linear Parameter Varying Systems Based on a Recurrent Neural Network. In: Dediu, AH., Lozano, M., Martín-Vide, C. (eds) Theory and Practice of Natural Computing. TPNC 2014. Lecture Notes in Computer Science, vol 8890. Springer, Cham. https://doi.org/10.1007/978-3-319-13749-0_22

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  • DOI: https://doi.org/10.1007/978-3-319-13749-0_22

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13748-3

  • Online ISBN: 978-3-319-13749-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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