Computer Science ›› 2017, Vol. 44 ›› Issue (10): 203-208.doi: 10.11896/j.issn.1002-137X.2017.10.037

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Study on Identification of Adaptive Inverse Control System Based on Dynamic Function Link Neural Network

HU Tao-tao, KANG Bo and SHAN Yao-nan   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Adaptive inverse control can eliminate the disturbance of the system and control the performance of dynamic response independently,and the performance of the adaptive inverse control system depends on the accuracy of the system object,the inverse object and the controller identification model.In this paper,the dynamic functional link neural network was proposed to realize the simultaneous on-line modeling of the adaptive inverse control system object and inverse object,and realize the off-line modeling of the controller,and the identification of model parameters was transformed into optimization of spatial parameters.Aiming at the destruction of convergent population structure by chaos initialization,this paper presented variable parameter chaotic particle swarm optimization algorithm to optimize the weights of the neural network.Through the simulation experiment,we can see that the modeling error based on the dynamic function link neural network is small and the identification accuracy based on the dynamic function link neural network is high.Compared with the current reference model adaptive control methods,the method in this paper can achieve better disturbance cancellation effect and improve the tracking response performance of the system,thus verifying the effectiveness and feasibility of the method.

Key words: Adaptive inverse control,Disturbance elimination,System identification,Dynamic function link neural network,Variable parameter,Chaos particle swarm optimization algorithm

[1] WIDROW B,WALACH E.自适应逆控制[M].刘绍棠,韩崇昭,译.西安:西安交通大学出版社,2000.
[2] 卢志刚,吴士昌,于灵慧.非线性自适应逆控制及其应用[M].北京:国防工业出版社,2004.
[3] COCHOFEL H J,WOOTEN D,PRINCIPE J.A neural network development environment for adaptive inverse control [J].IEEE World Congress on Computational Intelligence,1998,2:963-967.
[4] FAUSZ J L,CHELLABOINA V S,HADDAD M M.Inverse optimal adaptive control for nonlinear uncertain systems with exogenous disturbances [C]∥Proceedings of the 36th Confe-rence on Decision and Control.1998:2654-2659.
[5] XIE S Y.Adaptive inverse control and its application [D].Shandong:Shandong University,2007.(in Chinese) 解淑英.自适应逆控制及其应用研究[D].山东:山东大学,2007.
[6] LIU S,YU J S.Composite control combining neuro-fuzzy in-verse control with pid control and its application in a continuous stirred tank reactor[J].Control Theory and Applications,2001,18(5):769-773.
[7] MIREA,LETITIA.System identification using functional-linkneural networks with dynamic structure [C]∥15th Triennial World Congress.Barcelona,Spain,2002.
[8] LU H J.A new optimization algorithm based on Chaos [J].Zhejiang University Science A,2006,7(4):539-542.
[9] HAN H.The study of adaptive inverse control algorithm based on LMS [D].Changsha:Central South University,2008.(in Chinese) 韩华.基于LMS算法的自适应逆控制方法研究[D].长沙:中南大学,2008.
[10] LIU T,HAN H T,MA J.Study on model identification of Hammerstein sensor based on the function link neural network [J].Acta Metrologica Sinica,2015,36(1):97-101.(in Chinese) 刘滔,韩华亭,马婧.基于函数连接神经网络的传感器Hammerstein模型辨识研究[J].计量学报,2015,6(1):97-101.
[11] GUO Q M,HU H.The On-Line Monitoring System of Tower Crane Load Based on FLNN [J].Advanced Materials Research,2012,466-467:1373-1377.
[12] GONG Y L.The research of adaptive inverse control strategy for permanent magnet synchronous motor servo system [D].Changchu:Changchun University of Science and Technology,2013.(in Chinese) 宫玉琳.永磁同步电动机伺服系统自适应逆控制策略研究[D].长春:长春理工大学,2013.
[13] BAI Y.Research on high precision stabilized platform based on adaptive inverse control [D].Harbin:Harbin Institute of Technology,2012.(in Chinese) 白杨.基于自适应逆控制的高精度稳定平台的研究[D].哈尔滨:哈尔滨工业大学,2012.
[14] LI J W,CHENG Y M,CHEN K Z.Chaotic particle swarm optimization algorithm based on adaptive inertia weight[C]∥Control and Decision Conference.Beijing:IEEE,2014:1310-1315.
[15] PAN T S,DAO T K,CHU S C.Hybrid particle swarm optimization with bat algorithm [M].Switzerland:Springer International Publishing,2015:37-47.
[16] 胡寿松.自动控制原理[M].北京:科学出版社,2013
[17] LI J,QI X H,LIU X H,et al.Improved model reference adaptive control and its application in decoupling control [J].Journal of Electrical and Control,2015,19(5):112-120.(in Chinese) 李杰,齐晓慧,刘新海,等.改进模型参考自适应控制及其在解耦控制中的应用[J].电机与控制学报,2015,19(5):112-120.

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