高阶非线性不确定多智能体系统自适应RBF神经网络协同控制  

Adaptive RBF Neural Network Cooperative Control for High-order Nonlinear Multi-agent Systems With Uncertainties

作　　者：黄小龙 陈阳舟 HUANG Xiaolong;CHEN Yangzhou(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学信息学部,北京100124

出　　处：《北京工业大学学报》2020年第9期1008-1017,共10页Journal of Beijing University of Technology

基　　金：国家自然科学基金资助项目(61573030)。

摘　　要：针对外界环境的干扰及自身系统参数的不确定性对一类高阶非线性多智能体系统的影响,研究在领导跟随者网络模型下系统一致性的问题.该动力学系统中含有高阶积分器耦合未知非线性动力学和未知外部干扰,采用分布式自适应径向基函数(radial basis function,RBF)神经网络控制算法,确保神经网络对智能体非线性项进行在线逼近,滑模控制消除持续有界扰动等不确定项对稳定性的影响.首先设计出神经网络权值的自适应律,提出一种基于神经网络的自适应滑模控制协议,利用李雅普诺夫稳定性理论,证明该多智能体系统实现领导跟随一致性,并且最终有界跟踪误差的充分条件.在同质和异质多智能体2种条件下,仿真结果验证了提出方法的正确性.The leader-following consensus control problem is considered for a class of high-order nonlinear multi-agent systems with external disturbances and uncertain system parameters.The dynamics of systems with high-order integrator coupling unknown nonlinear dynamics and unknown external disturbance,adopts the distributed adaptive radial basis function(RBF)neural network control algorithm,to ensure that the neural network is employed to approximate the unknown nonlinear system functions on line,and eliminate persistent bounded disturbances such as uncertainties affecting stability.First of all,weights of neural network adaptive tuning law was designed,then a kind of adaptive sliding mode control protocol based on RBF neural network was proposed.Using Lyapunov stability theory,the sufficient condition of high-order nonlinear uncertain multi-agent system had leader-following to achieving consensus,and ultimately bounded residual errors was discussed.The results of umerical simulations of homogeneous and heterogeneous multi-agent systems are given to demonstrate the effectiveness of the proposed control methodology.

关 键 词：多智能体系统 高阶非线性系统 RBF神经网络 滑模控制 有限时间一致 协同控制 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]