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Neural Network Observer Based Consensus Control of Unknown Nonlinear Multi-agent Systems with Prescribed Performance and Input Quantization

  • Intelligent Control and Applications
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Abstract

This paper investigates the consensus tracking problem with predefined performances requirements for a class of unknown nonlinear multi-agent systems with hysteresis quantizer and external disturbances under a directed graph topology. Neural network observers are designed to estimate unmeasurable states and the the consensus tracking problem with performance requirements is transformed to a stabilization problem by prescribed performance error transformation schemes. The novel consensus protocol can be applied to a more general class of nonlinear multi-agent systems since the Lipschitz condition is avoided and state information is not required. It is strictly proved that all signals in the closed-loop systems are cooperatively uniformly ultimately bounded and both the transient and steady performances of the consensus tracking satisfy prescribed performance requirements. Finally, two numerical examples are presented to validate the effectiveness of the proposed strategy.

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Authors and Affiliations

  1. School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Zhengqing Shi, Chuan Zhou & Jian Guo

Authors
  1. Zhengqing Shi
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  2. Chuan Zhou
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  3. Jian Guo
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Corresponding author

Correspondence to Chuan Zhou.

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Recommended by Associate Editor Saleh Mobayen under the direction of Editor Jessie (Ju H.) Park.

This journal was supported by National Natural Science Foundation of China under Grant No. 61673219, 13th Five-Year Plan for Equipment Pre-research on Common Technology under Grant No.41412040101, Postgraduate Research & Practice Innovation Program of Jiangsu Province No. KYCX20_0295.

Zhengqing Shi is a Ph.D. candidate of Nanjing University of Science and Technology, China. His research interests include optimal consensus control and prescribed performance consensus control of multi-agent systems.

Chuan Zhou received his Ph.D. from Nanjing University of Aeronautics and Astronautics, China in 1999. Since 2001, he has been with Nanjing University of Science and Technology and he is a Professor in the School of Automation. His research interests include network control system, intelligent control and multi-agent systems.

Jian Guo received his Ph.D. from Nanjing University of Science and Technology, China in 2002. He is a Professor in the School of Automation. His research interests include robotic system and high performance servo system.

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Shi, Z., Zhou, C. & Guo, J. Neural Network Observer Based Consensus Control of Unknown Nonlinear Multi-agent Systems with Prescribed Performance and Input Quantization. Int. J. Control Autom. Syst. 19, 1944–1952 (2021). https://doi.org/10.1007/s12555-020-0326-8

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  • DOI: https://doi.org/10.1007/s12555-020-0326-8

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