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Iterative Learning Control of a Multiagent System under Random Perturbations

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Abstract

A multiagent system in which each of the agents is described by a linear discrete-time model with random perturbations (external random disturbances affecting the plant and measurement noises) is considered. Networked modifications of iterative learning control laws based on minimizing the deviations from a reference model and also based on the theory of stochastic stability of repetitive processes using the divergent method of vector Lyapunov functions are proposed. These modifications are compared with each other by an illustrative example of iterative learning control for a group of gantry robots.

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Funding

This work was supported by the Russian Foundation for Basic Research, project no. 19-08-00528_a.

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Authors and Affiliations

  1. Arzamas Polytechnic Institute of R.E. Alekseev Nizhny Novgorod State Technical University, Arzamas, Russia

    P. V. Pakshin, A. S. Koposov & J. P. Emelianova

Authors
  1. P. V. Pakshin
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  2. A. S. Koposov
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  3. J. P. Emelianova
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Corresponding authors

Correspondence to P. V. Pakshin, A. S. Koposov or J. P. Emelianova.

Additional information

This paper was recommended for publication by E.Ya. Rubinovich, a member of the Editorial Board

Russian Text © The Author(s), 2020, published in Avtomatika i Telemekhanika, 2020, No. 3, pp. 132–156.

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Pakshin, P.V., Koposov, A.S. & Emelianova, J.P. Iterative Learning Control of a Multiagent System under Random Perturbations. Autom Remote Control 81, 483–502 (2020). https://doi.org/10.1134/S0005117920030078

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  • DOI: https://doi.org/10.1134/S0005117920030078

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