多机械臂系统的自适应神经网络同步控制

Adaptive Neural Network Synchronization Control of Multiple Manipulator Systems

针对多个机械臂组成的网络化系统,本文基于命令滤波反步和自适应神经网络,对网络化机械臂系统的同步控制问题进行研究。建立了系统数学模型,对命令滤波控制器进行设计,运用命令滤波反步法,消除传统分布式反步控制的虚拟信号求导问题,并利用误差补偿信号,消除命令滤波产生的误差。同时,利用神经网络逼近系统的不确定非线性项,通过设计分布式控制器和自适应更新律,保证关节位置同步跟踪误差收敛到任意小的邻域内。为验证本文所提出的控制策略的准确性,在Matlab/Simulink仿真环境下进行仿真分析。仿真结果表明,在具有系统参数不确定性和外部扰动的情况下,该分布式控制方法可使网络化机械臂系统实现良好的位置同步跟踪。该研究对网络化机械臂系统的同步控制提供了新的思路,具有一定的实际应用价值。

In this paper,synchronization control of networked manipulator systems is studied based on command filtering backstepping and adaptive neural network.The system mathematical model is established,the command filtering controller is designed,the command filtering backstepping method is used to eliminate the derivative problem of the virtual signal of traditional distributed backstepping control,and the error compensation signal is used to eliminate the error caused by the command filtering.At the same time,the neural network is used to approximate the uncertain nonlinear dynamic of the system.By designing the distributed controller and the adaptive update law,the joint position tracking errors converge into arbitrarily small neighborhood.In order to verify the accuracy of the control strategy proposed in this paper,simulation analysis is carried out in MATLAB/Simulink simulation environment.The simulation results show that the distributed control method proposed in this paper can achieve good position tracking of the networked manipulator system with parameters uncertainty and external disturbances.This research provides a new idea for the synchronization control of networked manipulator systems and has certain practical application value.