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Distributed Adaptive Containment Control for a Class of Nonlinear Multiagent Systems With Input Quantization.

Chenliang Wang, Changyun Wen, Qinglei Hu

    IEEE Transactions on Neural Networks and Learning Systems
    |May 11, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study presents a distributed adaptive containment control strategy for nonlinear multiagent systems, successfully managing input quantization without needing quantizer parameters. The novel approach ensures system stability and precise error convergence.

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    Area of Science:

    • Control Engineering
    • Systems Science
    • Artificial Intelligence

    Background:

    • Distributed adaptive containment control is crucial for coordinating multiagent systems.
    • Input quantization presents a significant challenge in achieving robust control.
    • Existing methods often require strong assumptions on control gain matrices.

    Purpose of the Study:

    • To develop a distributed adaptive containment control scheme for nonlinear multiagent systems with input quantization.
    • To relax restrictive assumptions found in previous control gain matrix analyses.
    • To design controllers that do not require knowledge of quantizer parameters.

    Main Methods:

    • Utilizing matrix factorization and a novel matrix normalization technique.
    • Employing a hybrid approach combining sliding mode control and backstepping control.
    • Integrating neural networks to handle unknown system nonlinearities.
    • Introducing a linear time-varying model and similarity transformation to address quantization effects.

    Main Results:

    • Controllers were designed using a two-step method, accommodating unknown nonlinearities.
    • The proposed scheme effectively circumvents quantization obstacles without needing quantizer parameters.
    • All closed-loop signals were proven to be bounded.
    • Containment errors were steered into an arbitrarily small residual set.

    Conclusions:

    • The developed distributed adaptive containment control scheme is effective for nonlinear multiagent systems with input quantization.
    • The novel techniques relax existing assumptions, offering broader applicability.
    • The method ensures robust performance and accurate containment despite system uncertainties and quantization.