机械科学与技术2017，Vol.36Issue(3)：372-377,6.DOI:10.13433/j.cnki.1003-8728.2017.0308
不确定关节机器人模型的神经网络补偿自适应控制
Adaptively Controlling Neural Network Compensation with Uncertain Joint Robot Model
摘要
Abstract
In order to achieve the trajectory tracking control of a joint robot,because an uncertain joint robot's structural parameters cause a dynamic model's modeling errors and interfere with the working environment and the uncertain joint robot's resonant mode,the joint robot's dynamic model was divided into nominal model and error model.The error model was compensated by the RBF neural network,thus obtaining its estimation information.The neural network's output weights were adjusted adaptively according to the Lyapunov stability theory.The joint robot's adaptive neural network controller was used to solve the problems for the uncertain joint robot's dynamic system.Besides,the controller can gradually and stably track the desired trajectory though defining the Lyapunov function,being used to control a three-joint robot's torque.All the three joints can track the desired trajectory in 4 s.Tracking errors can gradually approach 0.Simulation and experimental results show that the RBF neural network can favorably approach modeling errors caused by uncertainties.关键词
关节机器人/不确定模型/RBF神经网络/自适应权值调整Key words
uncertain joint robot/modeling error/RBF neural network/output weight分类
信息技术与安全科学引用本文复制引用
钟斌..不确定关节机器人模型的神经网络补偿自适应控制[J].机械科学与技术,2017,36(3):372-377,6.基金项目
国家自然科学基金项目(51005246)资助 （51005246）
