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Neural Inverse Optimal Pinning Control of Output Trajectory Tracking for Uncertain Complex Networks with Nonidentical Nodes

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Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine

Part of the book series: Studies in Computational Intelligence ((SCI,volume 827))

Abstract

This chapter presents the development of a control scheme, we named as neural inverse optimal pinning control to achieve output trajectory tracking on uncertain complex networks with nonidentical nodes. A recurrent high order neural network is used to identify the unknown system dynamics of a small fraction of nodes (pinned ones) and by means of this neural model, an inverse optimal controller is designed to synchronize the whole network at an output desired reference. The proposed controller effectiveness is illustrated via simulations. The illustrative example is composed of a network of ten different chaotic nodes.

This work is supported by CONACYT, Mexico, Project 257200.

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Authors and Affiliations

  1. Electrical Engineering Department, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Av. del Bosque 1145, Col. El Bajío, 45019, Zapopan, Jalisco, Mexico

    Carlos J. Vega & Edgar N. Sanchez

Authors
  1. Carlos J. Vega
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  2. Edgar N. Sanchez
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Correspondence to Edgar N. Sanchez .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

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Vega, C.J., Sanchez, E.N. (2020). Neural Inverse Optimal Pinning Control of Output Trajectory Tracking for Uncertain Complex Networks with Nonidentical Nodes. In: Castillo, O., Melin, P. (eds) Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine. Studies in Computational Intelligence, vol 827. Springer, Cham. https://doi.org/10.1007/978-3-030-34135-0_24

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