输入时滞多智能体系统的神经网络固定时间跟踪控制

Xiaohong Zheng, Xiao‐Meng Li, Wenbin Xiao, Qi Zhou, Renquan Lu
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引用次数: 0

摘要

本文讨论了具有输入延迟的多智能体系统的固定时间一致性跟踪控制问题。首先,采用Pade逼近技术处理输入延迟。其次,利用命令滤波技术和神经网络对质量中的未知非线性进行重构。采用凸优化技术设计神经网络权值更新规律。为保证质量的暂态性能,采用定时控制,并采用曲线拟合的方法解决奇异性问题。在定时稳定性判据和李雅普诺夫稳定性定理下,证明了闭环系统的所有信号在定时有界。最后,通过仿真验证了算法的有效性。
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NN-based Fixed-Time Tracking Control for Multi-Agent Systems With Input Delays
This article discusses a fixed-time consensus tracking control problem for multi-agent systems (MASs) suffering from input delays. First, the Pade approximation technique is employed to deal with input delays. Second, unknown nonlinearities in MASs are reconstructed by command filtering technique and neural network (NN). Convex optimization technique is used to design NN weight update law. To guarantee the transient performance of MASs, fixed-time control is utilized, while the resulting singularity problem is solved by curve fitting method. Under the fixed-time stability criterion and Lyapunov stability theorem, it is shown that all signals of the closed-loop system are bounded in fixed time. Finally, the validity of the presented algorithm is checked by simulation.
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