Bounded Control of Robotic Manipulators via Fuzzy Logic Systems
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- 英文论文题名：Bounded Control of Robotic Manipulators via Fuzzy Logic Systems
- 论文作者：Linghuan Kong ; Wei He ; Yiting Dong ; Chenguang Yang ; Guang Li
- 英文论文作者：Linghuan Kong ; Wei He ; Yiting Dong ; Chenguang Yang ; Guang Li ; School of Automation Engineering and Center for Robotics ; University of Electronic Science and Technology of China ; School of Automation and Electrical Engineering ; University of Science and Technology Beijing ; Department of Mechanical Engineering ; Texas Tech University ; Zienkiewicz Centre for Computational Engineering ; Swansea University ; School of Engineering and Materials Science ; Queen Mary University of London
- 年：2017
- 作者机构：School of Automation Engineering and Center for Robotics, University of Electronic Science and Technology of China;School of Automation and Electrical Engineering, University of Science and Technology Beijing;Department of Mechanical Engineering, Texas Tech University;Zienkiewicz Centre for Computational Engineering, Swansea University;School of Engineering and Materials Science, Queen Mary University of London;
- 英文论文关键词：Fuzzy Logic System ; Neural Networks ; Input Constraints ; Robotic Manipulators
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（A）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
- 语种：英文
- 分类号：TP242
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：5
- 文件大小：998k
- 原文格式：D
- 会议级别：全国
摘要
In this paper, a fuzzy neural network controller is proposed for a class of robotic manipulators with unknown dynamics and input constraints. First, to cope with the unknown dynamics, fuzzy logic systems are designed to approximate it. Second,considering the case where an aggressive control input may lead to the poor performance, even resulting in instability in practice,the controller is designed with the function tanh(·), and the function has the ability to make the controller keep in the predefined range. At last, evidence based on the Lyapunov theory proves the system errors converge to a small range near zero. Simulations are carried out to show the effectiveness of the designed control.
In this paper, a fuzzy neural network controller is proposed for a class of robotic manipulators with unknown dynamics and input constraints. First, to cope with the unknown dynamics, fuzzy logic systems are designed to approximate it. Second,considering the case where an aggressive control input may lead to the poor performance, even resulting in instability in practice,the controller is designed with the function tanh(·), and the function has the ability to make the controller keep in the predefined range. At last, evidence based on the Lyapunov theory proves the system errors converge to a small range near zero. Simulations are carried out to show the effectiveness of the designed control.
In this paper, a fuzzy neural network controller is proposed for a class of robotic manipulators with unknown dynamics and input constraints. First, to cope with the unknown dynamics, fuzzy logic systems are designed to approximate it. Second,considering the case where an aggressive control input may lead to the poor performance, even resulting in instability in practice,the controller is designed with the function tanh(·), and the function has the ability to make the controller keep in the predefined range. At last, evidence based on the Lyapunov theory proves the system errors converge to a small range near zero. Simulations are carried out to show the effectiveness of the designed control.
引文
[1]W.He,S.S.Ge,Y.Li,E.Chew,and Y.S.Ng,“Neural network control of a rehabilitation robot by state and output feedback,”Journal of Intelligent&Robotic Systems,vol.80,no.1 ,pp.15-31,2015.
[2]Y.H.Kim,and F.L.Lewis,“Neural network output feedback control of robot manipulators,”IEEE Transactions on Robotics and Automation,vol.15,no.2,pp.301-309,1999.
[3]Z.Li,J.Li,and Y.Kang,“Adaptive robust coordinated control of multiple mobile manipulators interacting with rigid environments,”Automatica,vol.46 no.1,pp.2028-2034,2010.
[4]R.J.Wai,and R.Muthusamy,“Fuzzy-neural-network inherited slidingmode control for robot manipulator including actuator dynamics,”IEEE Transactions on Neural Networks and Learning Systems,vol.24,no.2,pp.274-287,2013.
[5]H.N.Wu,J.W.Wang,and H.X.Li,“Fuzzy boundary control design for a class of nonlinear parabolic distributed parameter systems,”IEEE Transactions on Fuzzy Systems,vol.22,no.3,pp.642-652,2014.
[6]J.W.Wang,H.N.Wang,and H.X.Li,“Distributed Proportional-Spatial Derivative Control of Nonlinear Parabolic Systems via Fuzzy PDE Modeling Approach,”IEEE Transactions on Systems,Man,Cybernetics-Part B:Cybernetics,vol.42,no.3,pp.927-938,2012.
[7]Z.Li,L.Ding,H.Gao,G.Duan,and C-Y.Su,“Trilateral Teleoperation of Adaptive Fuzzy Force/Motion Control for Nonlinear Teleoperators With Communication Random Delays”,IEEE Transactions on Fuzzy Systems,vol.21,no.4,pp.610-624,2013.
[8]G.B.Koo,J.B.Park,and Y.H.Joo,“Decentralized sampleddata fuzzy observer design for nonlinear interconnected systems”,IEEE Transactions on Fuzzy Systems,vol.24,no.3,pp.661-674,2016.
[9]W.S.Yu,M.Karhoub,T.S.Wu,and M.G.Her,“Delayed output feedback control for nonlinear systems with two-layer interval fuzzy observers”IEEE Transactions on Fuzzy Systems,vol.22,no.3,pp.611-630,2014.
[10]Q.Zhou,P.Shi,S.Xu,and H.Li,“Adaptive output feedback control for nonlinear time-delay systems by fuzzy approximation approach,”IEEE Transactions on Fuzzy Systems,vol.21,no.2,pp.301-313,2013.
[11]Y.Q.Jin,J.H.Wu,and W.J.Gu,“Adaptive Control for a Class of Nonaffine Systems Based on Fuzzy-Neural Approach,”Computational Engineering in Systems Applications,IMACS Multiconference pp.519-523,2016.
[12]C.L.P.Chen,Y.J.Liu,and G.X.Wen,“Fuzzy neural network-based adaptive control for a class of uncertain nonlinear stochastic systems,”IEEE Transactions on Cybernetics,vol.44,no.5,pp.583-593,2014.
[13]Z.Li,C.Yang,C.Yang,C.Y.Su,S.Deng,F.Sun,and W.Zhang,“Decentralized fuzzy control of multiple cooperating robotic manipulators with impedance interaction”,IEEE Transactions on Fuzzy Systems,vol.23,no.4,pp.1044-1056,2015.
[14]Y.J.Liu,S.C.Tong,and W.Wang,“Adaptive fuzzy output tracking control for a class of uncertain nonlinear systems”,Fuzzy Sets&Systems,vol.160,no.19,pp.2727-2754,2009.
[15]W.He,A.O.David,Z.Yin,and C.Sun,“Neural network control of a robotic manipulator with input deadzone and output constraint,”IEEE Transactions on Systems Man&Cybernetics Systems,vol.46,no.6,pp.759-770,2016.
[16]W.He,Y.Chen,and Z.Yin,“Adaptive neural network control of an uncertain robot with full-state constraints,”IEEE Transactions on Cybernetics,vol.46,no.3,pp.620-629,2016.
[17]L.Ding,S.Li,Y.J.Liu,H.Gao,C.Chen and Z.Deng,“Adaptive Neural Network-Based Tracking Control for Full-State Constrained Wheeled Mobile Robotic System,”IEEE Transactions on Systems,Man,and Cybernetics:Systems,vol.PP,no.99,pp.1-10 doi:10.1109/TSMC.2017.2677472.
[18]A.Malek,N.Hosseinipour-Mahani,“Solving Multiextremal Problems by Using Recurrent Neural Networks,”IEEE Transactions on Neural Networks and Learning Systems,vol.PP,no.99,pp.1-13,doi:10.1109/TNNLS.2017.2676046.
[19]J.Fei,C.Lu,“Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure,”IEEE Transactions on Neural Networks and Learning Systems,vol.PP,no.99,pp.1-12 doi:10.1109/TNNLS.2017.2672998.
[20]A.Zeiaee,R.Soltani-Zarrin and R.Langari,“A novel approach for tracking control of differential drive robots subject to hard input constraints,”2016 American Control Conference(ACC),Boston,pp.2098-2103,2016.
[21]W.He,and S.S.Ge,“Vibration Control of a Flexible String With Both Boundary Input and Output Constraints,”IEEE Transactions on Control Systems Technology,vol.23,no.4,pp.1245-1254,2015.
[2]Y.H.Kim,and F.L.Lewis,“Neural network output feedback control of robot manipulators,”IEEE Transactions on Robotics and Automation,vol.15,no.2,pp.301-309,1999.
[3]Z.Li,J.Li,and Y.Kang,“Adaptive robust coordinated control of multiple mobile manipulators interacting with rigid environments,”Automatica,vol.46 no.1,pp.2028-2034,2010.
[4]R.J.Wai,and R.Muthusamy,“Fuzzy-neural-network inherited slidingmode control for robot manipulator including actuator dynamics,”IEEE Transactions on Neural Networks and Learning Systems,vol.24,no.2,pp.274-287,2013.
[5]H.N.Wu,J.W.Wang,and H.X.Li,“Fuzzy boundary control design for a class of nonlinear parabolic distributed parameter systems,”IEEE Transactions on Fuzzy Systems,vol.22,no.3,pp.642-652,2014.
[6]J.W.Wang,H.N.Wang,and H.X.Li,“Distributed Proportional-Spatial Derivative Control of Nonlinear Parabolic Systems via Fuzzy PDE Modeling Approach,”IEEE Transactions on Systems,Man,Cybernetics-Part B:Cybernetics,vol.42,no.3,pp.927-938,2012.
[7]Z.Li,L.Ding,H.Gao,G.Duan,and C-Y.Su,“Trilateral Teleoperation of Adaptive Fuzzy Force/Motion Control for Nonlinear Teleoperators With Communication Random Delays”,IEEE Transactions on Fuzzy Systems,vol.21,no.4,pp.610-624,2013.
[8]G.B.Koo,J.B.Park,and Y.H.Joo,“Decentralized sampleddata fuzzy observer design for nonlinear interconnected systems”,IEEE Transactions on Fuzzy Systems,vol.24,no.3,pp.661-674,2016.
[9]W.S.Yu,M.Karhoub,T.S.Wu,and M.G.Her,“Delayed output feedback control for nonlinear systems with two-layer interval fuzzy observers”IEEE Transactions on Fuzzy Systems,vol.22,no.3,pp.611-630,2014.
[10]Q.Zhou,P.Shi,S.Xu,and H.Li,“Adaptive output feedback control for nonlinear time-delay systems by fuzzy approximation approach,”IEEE Transactions on Fuzzy Systems,vol.21,no.2,pp.301-313,2013.
[11]Y.Q.Jin,J.H.Wu,and W.J.Gu,“Adaptive Control for a Class of Nonaffine Systems Based on Fuzzy-Neural Approach,”Computational Engineering in Systems Applications,IMACS Multiconference pp.519-523,2016.
[12]C.L.P.Chen,Y.J.Liu,and G.X.Wen,“Fuzzy neural network-based adaptive control for a class of uncertain nonlinear stochastic systems,”IEEE Transactions on Cybernetics,vol.44,no.5,pp.583-593,2014.
[13]Z.Li,C.Yang,C.Yang,C.Y.Su,S.Deng,F.Sun,and W.Zhang,“Decentralized fuzzy control of multiple cooperating robotic manipulators with impedance interaction”,IEEE Transactions on Fuzzy Systems,vol.23,no.4,pp.1044-1056,2015.
[14]Y.J.Liu,S.C.Tong,and W.Wang,“Adaptive fuzzy output tracking control for a class of uncertain nonlinear systems”,Fuzzy Sets&Systems,vol.160,no.19,pp.2727-2754,2009.
[15]W.He,A.O.David,Z.Yin,and C.Sun,“Neural network control of a robotic manipulator with input deadzone and output constraint,”IEEE Transactions on Systems Man&Cybernetics Systems,vol.46,no.6,pp.759-770,2016.
[16]W.He,Y.Chen,and Z.Yin,“Adaptive neural network control of an uncertain robot with full-state constraints,”IEEE Transactions on Cybernetics,vol.46,no.3,pp.620-629,2016.
[17]L.Ding,S.Li,Y.J.Liu,H.Gao,C.Chen and Z.Deng,“Adaptive Neural Network-Based Tracking Control for Full-State Constrained Wheeled Mobile Robotic System,”IEEE Transactions on Systems,Man,and Cybernetics:Systems,vol.PP,no.99,pp.1-10 doi:10.1109/TSMC.2017.2677472.
[18]A.Malek,N.Hosseinipour-Mahani,“Solving Multiextremal Problems by Using Recurrent Neural Networks,”IEEE Transactions on Neural Networks and Learning Systems,vol.PP,no.99,pp.1-13,doi:10.1109/TNNLS.2017.2676046.
[19]J.Fei,C.Lu,“Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure,”IEEE Transactions on Neural Networks and Learning Systems,vol.PP,no.99,pp.1-12 doi:10.1109/TNNLS.2017.2672998.
[20]A.Zeiaee,R.Soltani-Zarrin and R.Langari,“A novel approach for tracking control of differential drive robots subject to hard input constraints,”2016 American Control Conference(ACC),Boston,pp.2098-2103,2016.
[21]W.He,and S.S.Ge,“Vibration Control of a Flexible String With Both Boundary Input and Output Constraints,”IEEE Transactions on Control Systems Technology,vol.23,no.4,pp.1245-1254,2015.