不确定非线性机械系统的自适应分解模糊控制
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摘要
为了保证系统较高的控制精度,就必须提高模糊系统的逼近精度,但其所需的大量的模糊规则会造成控制系统计算负担过重,不能满足实时性要求。为此,本文针对不确定机械系统的控制问题,设计了一种分解模糊系统用于系统中不确定函数的逼近和补偿,在此基础上针对不确定系统设计了鲁棒自适应控制律对非线性系统进行轨迹跟踪控制。仿真实验证明,本文设计的自适应分解模糊控制不但能对机械系统的未知部分进行实时补偿,并且比传统自适应模糊控制的控制精度更高,误差收敛更快,更有利于实时控制。
In order to improve the control precision of the system,it is necessary to improve the approximation accuracy of the fuzzy system,so a large number of fuzzy rules are needed. A large number of fuzzy rules will cause overburden of control system calculation and can not meet the requirement of real time control. In this paper,a new decomposition fuzzy system is used to compensate the uncertain of mechanical system,and a robust adaptive control law is designed to control the trajectory of the system. The simulation results show that,the adaptive decomposition fuzzy control designed in this paper can not only compensate for the unknown part of the mechanical system in real time,but also has higher control accuracy,faster convergence and faster real-time control than the traditional adaptive fuzzy control.
In order to improve the control precision of the system,it is necessary to improve the approximation accuracy of the fuzzy system,so a large number of fuzzy rules are needed. A large number of fuzzy rules will cause overburden of control system calculation and can not meet the requirement of real time control. In this paper,a new decomposition fuzzy system is used to compensate the uncertain of mechanical system,and a robust adaptive control law is designed to control the trajectory of the system. The simulation results show that,the adaptive decomposition fuzzy control designed in this paper can not only compensate for the unknown part of the mechanical system in real time,but also has higher control accuracy,faster convergence and faster real-time control than the traditional adaptive fuzzy control.
引文
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[15] Liu Y J,Tong S C. Adaptive fuzzy control for a class of unknown nonlinear dynamical systems[J]. Fuzzy Sets and Systems,2015,263:49-70
[16] Wang H O,Tanaka K,Griffin M F. An approach to fuzzy control of nonlinear systems:stability and design issues[J]. IEEE Transactions on Fuzzy Systems,1996,4(1):14-23
[17] Hwang C L,Chiang C C,Yeh Y W. Adaptive fuzzy hierarchical sliding-mode control for the trajectory tracking of uncertain underactuated nonlinear dynamic systems[J]. IEEE Transactions on Fuzzy Systems,2014,22(2):286-299
[18]吴东苏,顾宏斌.基于模糊干扰观测器的电动Stewart平台自适应模糊控制[J].机械科学与技术,2008,27(6):757-763Wu D S, Gu H B. Adaptive fuzzy control for an electrical driven stewart platform based on fuzzy disturbance observer[J]. Mechanical Science and Technology for Aerospace Engineering,2008,27(6):757-763(in Chinese)
[19] Wang L X,Mendel J M. Fuzzy basis functions,universal approximation,and orthogonal least-squares learning[J].IEEE Transactions on Neural Networks,1992,3(5):807-814
[20]刘金琨.机器人控制系统的设计与MATLAB仿真[M].北京:清华大学出版社,2008Liu J K. Robot control system design and MATLAB simulation[M]. Beijing:Tsinghua University Press,2008(in Chinese)
[21] Yoo B K,Ham W C. Adaptive control of robot manipulator using fuzzy compensator[J]. IEEE Transactions on Fuzzy Systems,2000,8(2):186-199
[22] Hsueh Y C,Su S F,Chen M C. Decomposed fuzzy systems and their application in direct adaptive fuzzy control[J]. IEEE Transactions on Cybernetics,2014,44(10):1772-1783
[23]刘金琨.智能控制[M].北京:电子工业出版社出,2014Liu J K. Intelligent control[M]. Beijing:Publishing House of Electronics Industry,2014(in Chinese)
[2] Hu Q L,Ma G F,Xie L H. Robust and adaptive variable structure output feedback control of uncertain systems with input nonlinearity[J]. Automatica,2008,44(2):552-559
[3] Xiao B,Yin S,Kaynak O. Tracking control of robotic manipulators with uncertain kinematics and dynamics[J].IEEE Transactions on Industrial Electronics,2016,63(10):6439-6449
[4] Jumarie G. Tracking control of mechanical systems via sliding Lagrangian[J]. Journal of Intelligent and Robotic Systems,1995,13(2):181-199
[5] Xu R,ner. Sliding mode control of a class of underactuated systems[J]. Automatica,2008,44(1):233-241
[6] Pisano A,Usai E. Sliding mode control:a survey with applications in math[J]. Mathematics and Computers in Simulation,2011,81(5):954-979
[7] Swaroop D,Hedrick J K,Yip P P,et al. Dynamic surface control for a class of nonlinear systems[J]. IEEE Transactions on Automatic Control, 2000,45(10):1893-1899
[8] Riachy S,Orlov Y,Floquet T,et al. Second-order sliding mode control of underactuated mechanical systems I:local stabilization with application to an inverted pendulum[J]. International Journal of Robust and Nonlinear Control,2008,18(4-5):529-543
[9] Sun L,Zheng Z W. Finite-time sliding mode trajectory tracking control of uncertain mechanical systems[J].Asian Journal of Control,2017,19(1):399-404
[10] Kovaleva A,Akulenko L. Approximation of escape time for Lagrangian systems with fast noise[J]. IEEE Transactions on Automatic Control, 2007,52(12):2338-2341
[11] Lightbody G,Irwin G W. Direct neural model reference adaptive control[J]. IEE Proceedings-Control Theory and Applications,1995,142(1):31-43
[12] Wang F Y,Kim H M. Implementing adaptive fuzzy logic controllers with neural networks:a design paradigm[J].Journal of Intelligent and Fuzzy Systems,1995,3(2):165-180
[13]吴玉香,王聪.基于确定学习的机器人任务空间自适应神经网络控制[J].自动化学报,2013,39(6):806-815Wu Y X,Wang C. Deterministic learning based adaptive network control of robot in task space[J]. Acta Automatica Sinica,2013,39(6):806-815(in Chinese)
[14]吴玉香,王聪.不确定机器人的自适应神经网络控制与学习[J].控制理论与应用,2013,30(8):990-997Wu Y X,Wang C. Adaptive neural network control and learning for uncertain robot[J]. Control Theory&Applications,2013,30(8):990-997(in Chinese)
[15] Liu Y J,Tong S C. Adaptive fuzzy control for a class of unknown nonlinear dynamical systems[J]. Fuzzy Sets and Systems,2015,263:49-70
[16] Wang H O,Tanaka K,Griffin M F. An approach to fuzzy control of nonlinear systems:stability and design issues[J]. IEEE Transactions on Fuzzy Systems,1996,4(1):14-23
[17] Hwang C L,Chiang C C,Yeh Y W. Adaptive fuzzy hierarchical sliding-mode control for the trajectory tracking of uncertain underactuated nonlinear dynamic systems[J]. IEEE Transactions on Fuzzy Systems,2014,22(2):286-299
[18]吴东苏,顾宏斌.基于模糊干扰观测器的电动Stewart平台自适应模糊控制[J].机械科学与技术,2008,27(6):757-763Wu D S, Gu H B. Adaptive fuzzy control for an electrical driven stewart platform based on fuzzy disturbance observer[J]. Mechanical Science and Technology for Aerospace Engineering,2008,27(6):757-763(in Chinese)
[19] Wang L X,Mendel J M. Fuzzy basis functions,universal approximation,and orthogonal least-squares learning[J].IEEE Transactions on Neural Networks,1992,3(5):807-814
[20]刘金琨.机器人控制系统的设计与MATLAB仿真[M].北京:清华大学出版社,2008Liu J K. Robot control system design and MATLAB simulation[M]. Beijing:Tsinghua University Press,2008(in Chinese)
[21] Yoo B K,Ham W C. Adaptive control of robot manipulator using fuzzy compensator[J]. IEEE Transactions on Fuzzy Systems,2000,8(2):186-199
[22] Hsueh Y C,Su S F,Chen M C. Decomposed fuzzy systems and their application in direct adaptive fuzzy control[J]. IEEE Transactions on Cybernetics,2014,44(10):1772-1783
[23]刘金琨.智能控制[M].北京:电子工业出版社出,2014Liu J K. Intelligent control[M]. Beijing:Publishing House of Electronics Industry,2014(in Chinese)