摘要
External disturbances or inaccurate mathematical model built will inevitably impose a disadvantageous effect on the robot system,which generates positioning errors,vibrations,as well as weakening control performances of the system. The strategy of combining adaptive radial basis function( RBF) neural network control and composite nonlinear feedback( CNF) control is studied,and a robot CNF controller based on RBF neural network compensation is proposed. The core is to use RBF neural network control to approach the uncertainty of the system online,as the compensation term of the CNF controller,and make full use of the advantages of the two control methods to reduce the influence of uncertain factors on the performance of the system. The convergence of closed-loop system is proved. Simulation results demonstrate the effectiveness of this strategy.
External disturbances or inaccurate mathematical model built will inevitably impose a disadvantageous effect on the robot system,which generates positioning errors,vibrations,as well as weakening control performances of the system. The strategy of combining adaptive radial basis function( RBF) neural network control and composite nonlinear feedback( CNF) control is studied,and a robot CNF controller based on RBF neural network compensation is proposed. The core is to use RBF neural network control to approach the uncertainty of the system online,as the compensation term of the CNF controller,and make full use of the advantages of the two control methods to reduce the influence of uncertain factors on the performance of the system. The convergence of closed-loop system is proved. Simulation results demonstrate the effectiveness of this strategy.
作者
GONG chenglong
JIANG Yuan
LU Ke
公成龙;蒋沅;吕科(College of Information Engineering,Nanchang Hangkong University;School of Engineering Management and Information Technology,University of Chinese Academy of Sciences;Non-destructive Testing Technology Ministry of Education Key Laboratory,Nanchang Hangkong University)
基金
National Natural Science Foundation of China(Nos.61663030,61663032)
Natural Science Foundation of Jiangxi Province,China(No.20142BAB207021)
the Foundation of Jiangxi Educational Committee,China(No.GJJ150753)
the Innovation Fund Designated for Graduate Students of Nanchang Hangkong University,China(Nos.YC2017027,2018YBXG014)
the Open Fund of Key Laboratory of Image Processing and Pattern Recognition of Jiangxi Province(Nanchang Hangkong University),China(No.TX201404003)
Key Laboratory of Nondestructive Testing(Nanchang Hangkong University),Ministry of Education,China(No.ZD29529005)
