精确估计下的多智能体系统漏斗复合控制

Funnel composite control for multi-agent systems with accurate estimation

中文关键词  多智能体系统  神经网络  扰动观测器  复合估计模型

英文关键词  multi-agent systems  neural network  disturbance observer  composite estimation model

中文摘要

      针对一类具有时变扰动的非线性多智能体系统, 研究其在有向拓扑下的一致性跟踪问题, 提出一种基于精 确估计的复合自适应预设有限时间(PFT)漏斗控制方法. 首先, 构建一种新的PFT漏斗控制, 使跟踪误差约束在PFT 漏斗边界内. 其次, 采用神经网络(NN)逼近系统的未知非线性, 并利用NN逼近信息设计扰动观测器, 建立基于NN和 扰动观测器的复合估计模型, 将得到的预测误差引入NN权值的复合更新律中, 实现对未知非线性和时变扰动的精 确估计. 然后, 利用动态面技术和误差补偿机制, 在解决传统反步法“计算爆炸”问题的同时, 消除滤波器误差对系 统的影响. 最后, 通过Lyapunov稳定性理论证明闭环系统所有信号均为有界的, 并通过仿真实验验证控制方法的有 效性.

英文摘要

      For a class of nonlinear multi-agent systems with time-varying disturbances, the problem of consensus tracking in directed topology is studied, and a composite adaptive preassigned finite-time (PFT) funnel control based on accurate estimation is proposed. Firstly, a new PFT funnel control is used to constrain the tracking error within the boundary of PFT funnel. Secondly, neural networks (NNs) are employed to approximate the unknown nonlinearities of the system, and the disturbance observer is constructed with NN approximation information. The composite estimation model with NN and the disturbance observer is built to obtain the prediction error. The prediction error is introduced into the NN weight updating composite law to accurately estimate the unknown nonlinearities and time-varying disturbances. Then, the dynamic surface control technique is utilized to solve the “explosion of complexity” problem caused by the traditional backstepping technique, and the error compensation method is utilized to solve the influence of filter errors. Finally, according to the Lyapunov stability theory, all signals in the closed-loop system are bounded, and the effectiveness of the proposed control algorithm is verified by simulation results.