In order to suppress the influence of uncertain factors on robot system and enable an uncertain robot system to track the reference input accurately,a strategy of combining composite nonlinear feedback(CNF)control and...In order to suppress the influence of uncertain factors on robot system and enable an uncertain robot system to track the reference input accurately,a strategy of combining composite nonlinear feedback(CNF)control and adaptive fuzzy control is studied,and a robot CNF controller based on adaptive fuzzy compensation is proposed.The key of this strategy is to use adaptive fuzzy 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 the closed-loop system is proved by feedback linearization and Lyapunov theory.The final simulation results confirm the effectiveness of this plan.展开更多
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 ...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.展开更多
基金Supported by the National Natural Science Foundation of China(No.61663030,61663032)Natural Science Foundation of Jiangxi Province(No.20142BAB207021)+4 种基金the Foundation of Jiangxi Educational Committee(No.GJJ150753)the Innovation Fund Designated for Graduate Students of Nanchang Hangkong University(No.YC2017027)the Open Fund of Key Laboratory of Image Processing and Pattern Recognition of Jiangxi Province(Nanchang Hangkong University)(No.TX201404003)Key Laboratory of Nondestructive Testing(Nanchang Hangkong University),Ministry of Education(No.ZD29529005)the Reform Project of Degree and Postgraduate Education in Jiangxi(No.JXYJG-2017-131)
文摘In order to suppress the influence of uncertain factors on robot system and enable an uncertain robot system to track the reference input accurately,a strategy of combining composite nonlinear feedback(CNF)control and adaptive fuzzy control is studied,and a robot CNF controller based on adaptive fuzzy compensation is proposed.The key of this strategy is to use adaptive fuzzy 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 the closed-loop system is proved by feedback linearization and Lyapunov theory.The final simulation results confirm the effectiveness of this plan.
基金National Natural Science Foundation of China(Nos.61663030,61663032)Natural Science Foundation of Jiangxi Province,China(No.20142BAB207021)+3 种基金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)
文摘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.
