Distributed adaptive practical time-varying tracking control for second-order nonlinear multi-agent system using neural networks

Jianglong Yu, Xiwang Dong, Qingdong Li, Fei Liu, Z. Ren, H. Ni
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引用次数: 5

Abstract

Practical adaptive time-varying formation tracking problems for second-order nonlinear multi-agent systems are investigated using neural networks, where the time-varying formation tracking error can be arbitrarily small. Different from the previous work, the states of followers form a predefined time-varying formation while tracking the states of the leader with unknown control input. Besides, the dynamics of each agent has heterogeneous nonlinearity. Firstly, for the case where the control input of the leader is unknown, a nonlinear practical time-varying formation tracking protocol using adaptive neural networks is proposed which is constructed using only local neighboring information. Secondly, sufficient conditions for the second-order nonlinear multi-agent systems to achieve practical time-varying formation are presented, where a novel practical time-varying formation tracking feasibility condition is given. Thirdly, an approach is presented to design the control parameters for distributed practical formation tracking control protocol. The stability of the closed-loop system is proven by using the Lyapunov stability theory. Finally, simulation results are given to illustrate the effectiveness of the obtained results.
二阶非线性多智能体系统的分布式自适应实用化时变跟踪控制
利用神经网络研究了二阶非线性多智能体系统的自适应时变队列跟踪问题,该问题的时变队列跟踪误差可以任意小。与以往不同的是,在控制输入未知的情况下,follower的状态在跟踪leader的状态时形成一个预定义的时变队形。此外,各智能体的动力学具有非均质非线性。首先,针对leader控制输入未知的情况,提出了一种仅利用局部邻域信息构建自适应神经网络的非线性实用化时变队列跟踪协议;其次,给出了二阶非线性多智能体系统实现时变编队的充分条件,给出了一种新颖的时变编队跟踪可行性条件。第三,提出了一种分布式实用编队跟踪控制协议的控制参数设计方法。利用李雅普诺夫稳定性理论证明了闭环系统的稳定性。最后给出了仿真结果,验证了所得结果的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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