农业机械学报2018，Vol.49Issue(2)：395-404,240,11.DOI:10.6041/j.issn.1000-1298.2018.02.051
机械臂神经网络非奇异快速终端滑模控制
Nonsingular Fast Terminal Sliding Mode Control of Robotic Manipulators Based on Neural Networks
摘要
Abstract
A nonsingular fast terminal sliding mode adaptive controller based on RBF neural network was proposed for trajectory tracking control of multi degree of freedom manipulator with slow convergence speed and low tracking precision.Firstly,the nonsingular fast terminal sliding mode hypersurface was adopted in the control scheme and the continuous terminal attractor was introduced into the switch control,which made the system converge to the equilibrium point in a finite time.Secondly,the adaptive RBF neural network was used to approximate the unknown nonlinear dynamics of the system,the adaptive compensation mechanism of approximation error and adaptive law of weights of neural networks were designed to realize the model free control.The global asymptotic stability and finite time convergence of the closed-loop system were proved by Lyapunov theory.Finally,the control method was applied to Denso serial manipulator for experimental verification,the effect of transmission delay on the experimental results was analyzed and the solution was proposed.The simulation and experimental results demonstrated that the proposed control method can improve the convergence speed and the tracking accuracy of the system effectively,and enhance the robustness of the external disturbance.At the same time,it can weaken the chattering of the system and enhance the real-time control.关键词
机械臂/轨迹跟踪/终端滑模/神经网络/有限时间收敛Key words
robotic manipulators/trajectory tracking/terminal sliding mode/neural network/finite time convergence分类
信息技术与安全科学引用本文复制引用
吴爱国,刘海亭,董娜..机械臂神经网络非奇异快速终端滑模控制[J].农业机械学报,2018,49(2):395-404,240,11.基金项目
国家自然科学基金项目(61403274)和天津市智能制造科技重大专项(15ZXZNGX00160) （61403274）
