Neural Networks-Based Distributed Adaptive Control of Nonlinear Multiagent Systems

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Neural Networks-Based Distributed Adaptive Control of Nonlinear Multiagent Systems

机译：基于神经网络的非线性多主体系统分布式自适应控制

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The cooperative control problem of nonlinear multiagent systems is studied in this paper. The followers in the communication network are subject to unmodeled dynamics. A fully distributed neural-networks-based adaptive control strategy is designed to guarantee that all the followers are asymptotically synchronized to the leader, and the synchronization errors are within a prescribed level, where some global information, such as minimum and maximum singular value of graph adjacency matrix, is not necessarily to be known. Based on the Lyapunov stability theory and algebraic graph theory, the stability analysis of the resulting closed-loop system is provided. Finally, an numerical example illustrates the effectiveness and potential of the proposed new design techniques.

机译：研究了非线性多主体系统的协同控制问题。通信网络中的追随者会受到未建模动态的影响。设计了基于完全分布式神经网络的自适应控制策略，以确保所有跟随者都渐近地与领导者同步，并且同步误差在规定的水平内，其中一些全局信息（例如，图的最小和最大奇异值）邻接矩阵不一定是已知的。基于李雅普诺夫稳定性理论和代数图论，提供了所得闭环系统的稳定性分析。最后，一个数值示例说明了所提出的新设计技术的有效性和潜力。

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《Neural Networks and Learning Systems, IEEE Transactions on》 |2020年第3期|1010-1021|共12页
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Yangzhou Univ Coll Informat Engn Yangzhou 225127 Jiangsu Peoples R China;

Univ Adelaide Sch Elect & Elect Engn Adelaide SA 5005 Australia;

Kochi Univ Technol Sch Syst Engn Kochi 7828502 Japan;

Tokai Univ Grad Sch Sci & Technol Kumamoto 8628652 Japan;

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正文语种 eng
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关键词
Adaptive control; Synchronization; Decentralized control; Nonlinear dynamical systems; Laplace equations; Learning systems; Multi-agent systems; Cooperative control; leader-following consensus; prescribed performance; unmodeled dynamics;

机译：自适应控制同步;分散控制;非线性动力系统;拉普拉斯方程学习系统;多代理系统;合作控制;领导者遵循共识;订明的表现;未建模的动力学;

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1. Consensus Tracking Control for Distributed Nonlinear Multiagent Systems via Adaptive Neural Backstepping Approach [J] . Shang Yun, Chen Bing, Lin Chong Systems, Man and Cybernetics, IEEE Transactions on . 2020,第7期

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2. Neural-network-based distributed adaptive asymptotically consensus tracking control for nonlinear multiagent systems with input quantization and actuator faults [J] . Li Yu, Wang Chaoli, Cai Xuan, Neurocomputing . 2019,第JULa15期

机译：输入量化和执行器故障的非线性多智能体系统基于神经网络的分布式自适应渐近共识跟踪控制
3. Neural-network-based distributed adaptive asymptotically consensus tracking control for nonlinear multiagent systems with input quantization and actuator faults [J] . Li Yu, Wang Chaoli, Cai Xuan, Neurocomputing . 2019,第Jul15期

机译：基于神经网络的基于网络的分布式自适应渐近渐近渐近渐近渐近与输入量化和执行器故障的非线性多算系统的分布式渐近兼容性兼容性跟踪控制
4. CMAC neural networks-based adaptive control for discrete-time nonlinear systems with unmatched uncertainties by backstepping [C] . You-An Zhang, Yun-An Hu Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on . 2000

机译：基于CMAC神经网络的不确定时间离散非线性系统的自适应控制
5. A neural networks-based in-process adaptive surface roughness control (NN-IASRC) system in end-milling operations. [D] . Huang, Po-Tsang Bernie. 2002

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6. A Physics-driven Neural Networks-based Simulation System (PhyNNeSS) for multimodal interactive virtual environments involving nonlinear deformable objects [O] . Suvranu De, Dhannanjay Deo, Ganesh Sankaranarayanan, -1

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7. Leader-Following Consensus for Second-Order Nonlinear Multiagent Systems with Input Saturation via Distributed Adaptive Neural Network Iterative Learning Control [O] . Xiongfeng Deng, Xiuxia Sun, Shuguang Liu, 2019

机译：通过分布式自适应神经网络迭代学习控制对具有输入饱和的二阶非线性多元素系统的领导者达成达人

Neural Networks-Based Distributed Adaptive Control of Nonlinear Multiagent Systems

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