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Learning Impulsive Pinning Control of Complex Networks

Author

Listed:
  • Alma Y. Alanis

    (Computer Sciences Department, Universidad de Guadalajara, Guadalajara 44430, Mexico)

  • Daniel Ríos-Rivera

    (Computer Sciences Department, Universidad de Guadalajara, Guadalajara 44430, Mexico)

  • Edgar N. Sanchez

    (Electrical Engineering Department, CINVESTAV, Unidad Guadalajara, Zapopan 45017, Mexico)

  • Oscar D. Sanchez

    (Computer Sciences Department, Universidad de Guadalajara, Guadalajara 44430, Mexico)

Abstract

In this paper, we present an impulsive pinning control algorithm for discrete-time complex networks with different node dynamics, using a linear algebra approach and a neural network as an identifier, to synthesize a learning control law. The model of the complex network used in the analysis has unknown node self-dynamics, linear connections between nodes, where the impulsive dynamics add feedback control input only to the pinned nodes. The proposed controller consists of the linearization for the node dynamics and a reorder of the resulting quadratic Lyapunov function using the Rayleigh quotient. The learning part of the control is done with a discrete-time recurrent high order neural network used for identification of the pinned nodes, which is trained using an extended Kalman filter algorithm. A numerical simulation is included in order to illustrate the behavior of the system under the developed controller. For this simulation, a 20-node complex network with 5 different node dynamics is used. The node dynamics consists of discretized versions of well-known continuous chaotic attractors.

Suggested Citation

  • Alma Y. Alanis & Daniel Ríos-Rivera & Edgar N. Sanchez & Oscar D. Sanchez, 2021. "Learning Impulsive Pinning Control of Complex Networks," Mathematics, MDPI, vol. 9(19), pages 1-9, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2436-:d:648106
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    References listed on IDEAS

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