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  Optimized Adaptive Finite-Time Consensus Control for Stochastic Nonlinear Multiagent Systems With Non-Affine Nonlinear Faults

Wang, X., Guang, W., Huang, T., Kurths, J. (2023 online): Optimized Adaptive Finite-Time Consensus Control for Stochastic Nonlinear Multiagent Systems With Non-Affine Nonlinear Faults. - IEEE Transactions on Automation Science and Engineering.
https://doi.org/10.1109/TASE.2023.3306101

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 Creators:
Wang, Xin1, Author
Guang, Weiwei1, Author
Huang, Tingwen1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: This article studies the optimized adaptive finite-time consensus control issue for stochastic nonlinear multiagent systems subject to non-affine nonlinear faults. Under the architecture of the adaptive optimized backstepping method, this article develops the neural-network-based simplified reinforcement learning algorithm with an identifier-critic-actor structure, where the identifier, critic and actor are put forward to estimate unknown dynamics, evaluate system performance and implement control behavior, respectively. Then, the Butterworth low-pass filter is introduced to compensate for the adverse effects brought by non-affine nonlinear faults. Furthermore, it is verified by Itô differential equation and the finite-time theory that the closed-loop system is semi-global finite-time stable in probability. Finally, the effectiveness of the control algorithm is illustrated by simulation examples. Note to Practitioners —This paper was motivated by the problem of finite-time convergence is one of significance performance index in many practical application. For systems with high transient performance standards, such as robotic systems, manipulator systems and unmanned aerial systems, finite time convergence is of practical importance. Accordingly, distinguished from the previous investigation results, this article develops the neural-network (NN)-based simplified reinforcement learning (RL) algorithm with an identifier-critic-actor structure, where the identifier, critic and actor are put forward to estimate unknown dynamics, evaluate system performance and implement control behavior, respectively. We believe that the novel research method will bring a research spring for the constrained systems. Preliminary simulation experiments suggest that this approach is feasible. In future research, we will address the fixed time control protocol designs for nonlinear multi-agent systems.

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Language(s): eng - English
 Dates: 2023-08-23
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/TASE.2023.3306101
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
 Degree: -

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Title: IEEE Transactions on Automation Science and Engineering
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1558-3783
Publisher: Institute of Electrical and Electronics Engineers (IEEE)