Neural adaptive chaotic control with constrained input using state and output feedback

doi:10.1088/1674-1056/24/1/010501

›› 2015, Vol. 24 ›› Issue (1): 10501-010501.doi: 10.1088/1674-1056/24/1/010501

Neural adaptive chaotic control with constrained input using state and output feedback

高士根^a, 董海荣^a, 孙绪彬^b, 宁滨^a

a State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;
b School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

收稿日期:2014-05-16 修回日期:2014-06-28 出版日期:2015-01-05 发布日期:2015-01-05
基金资助:
Project supported by the National High Technology Research and Development Program of China (Grant No. 2012AA041701), the Fundamental Research Funds for Central Universities of China (Grant No. 2013JBZ007), the National Natural Science Foundation of China (Grant Nos. 61233001, 61322307, 61304196, and 61304157), and the Research Program of Beijing Jiaotong University, China (Grant No. RCS2012ZZ003).

Neural adaptive chaotic control with constrained input using state and output feedback

Gao Shi-Gen (高士根)^a, Dong Hai-Rong (董海荣)^a, Sun Xu-Bin (孙绪彬)^b, Ning Bin (宁滨)^a

a State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;
b School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

Received:2014-05-16 Revised:2014-06-28 Online:2015-01-05 Published:2015-01-05
Contact: Dong Hai-Rong E-mail:hrdong@bjtu.edu.cn
Supported by:
Project supported by the National High Technology Research and Development Program of China (Grant No. 2012AA041701), the Fundamental Research Funds for Central Universities of China (Grant No. 2013JBZ007), the National Natural Science Foundation of China (Grant Nos. 61233001, 61322307, 61304196, and 61304157), and the Research Program of Beijing Jiaotong University, China (Grant No. RCS2012ZZ003).

摘要/Abstract

摘要： This paper presents neural adaptive control methods for a class of chaotic nonlinear systems in the presence of constrained input and unknown dynamics. To attenuate the influence of constrained input caused by actuator saturation, an effective auxiliary system is constructed to prevent the stability of closed loop system from being destroyed. Radial basis function neural networks (RBF-NNs) are used in the online learning of the unknown dynamics, which do not require an off-line training phase. Both state and output feedback control laws are developed. In the output feedback case, high-order sliding mode (HOSM) observer is utilized to estimate the unmeasurable system states. Simulation results are presented to verify the effectiveness of proposed schemes.

关键词: chaotic control, neural adaptive control, constrained input

Abstract: This paper presents neural adaptive control methods for a class of chaotic nonlinear systems in the presence of constrained input and unknown dynamics. To attenuate the influence of constrained input caused by actuator saturation, an effective auxiliary system is constructed to prevent the stability of closed loop system from being destroyed. Radial basis function neural networks (RBF-NNs) are used in the online learning of the unknown dynamics, which do not require an off-line training phase. Both state and output feedback control laws are developed. In the output feedback case, high-order sliding mode (HOSM) observer is utilized to estimate the unmeasurable system states. Simulation results are presented to verify the effectiveness of proposed schemes.

Key words: chaotic control, neural adaptive control, constrained input

中图分类号: (Control of chaos, applications of chaos)

05.45.Gg

高士根, 董海荣, 孙绪彬, 宁滨. Neural adaptive chaotic control with constrained input using state and output feedback[J]. , 2015, 24(1): 10501-010501.

Gao Shi-Gen (高士根), Dong Hai-Rong (董海荣), Sun Xu-Bin (孙绪彬), Ning Bin (宁滨). Neural adaptive chaotic control with constrained input using state and output feedback[J]. Chin. Phys. B, 2015, 24(1): 10501-010501.

参考文献 36

[1]	Lorenz E 1963 J. Atmos. Ser. 20 130
[2]	Rössler O E 1976 Phys. Lett. A 57 397
[3]	Chua L O, Komuro M and Matsumoto T 1986 IEEE Trans. Circ. Syst. 33 1072
[4]	Sprott J C 1994 Phys. Rev. E 50 R647
[5]	Chen G R and Ueta T 1999 Int. J. Bifurcation Chaos 9 1465
[6]	Lü J H and Chen G R 2002 Int. J. Bifurcation Chaos 12 3659
[7]	Ott E, Grebogi C and Yorke J A 1990 Phys. Rev. Lett. 64 1196
[8]	Ma T and Fu J 2011 Chin. Phys. B 20 050511
[9]	Liu D F, Wu Z Y and Ye Q L 2014 Chin. Phys. B 23 040504
[10]	Ma T, Zhang H and Fu J 2008 Chin. Phys. B 17 4407
[11]	Song R Z, Xiao W D, Sun C Y and Wei Q L 2013 Chin. Phys. B 22 090502
[12]	Song R Z, Xiao W D and Wei Q L 2014 Chin. Phys. B 23 050504
[13]	Yang D D 2014 Chin. Phys. B 23 010504
[14]	Kwon O M, Park M J, Park J H, Lee S M and Cha E J 2013 Chin. Phys. B 22 110504
[15]	Fu S H, Lu Q S and Du Y 2012 Chin. Phys. B 21 060507
[16]	Zhu D R, Liu C X and Yan B N 2012 Chin. Phys. B 21 090509
[17]	Song Y Z, Zhao G Z and Qi D L 2006 Chin. Phys. 15 2266
[18]	Yassen M T 2006 Chaos Soliton. Fract. 27 537
[19]	Li H Y and Hu Y A 2011 Chin. Phys. Lett. 28 120508
[20]	Alasty A and Salarieh H 2007 Chaos Soliton. Fract. 31 292
[21]	Yau H T and Chen C L 2007 Chaos Soliton. Fract. 34 1567
[22]	Lin D, Wang X and Yao Y 2012 Nonlinear Dynamics 67 2889
[23]	Zhou J and Wen C 2008 Adaptive Backstepping Control of Uncertain Systems (Berlin, Heidelberg: Springer) pp. 9-31
[24]	Swaroop D, Hedrick J K, Yip P P and Gerdes J C 2000 IEEE Trans. Autom. Control 45 1893
[25]	Park J H, Kim S H and Moon C J 2009 IEEE Trans. Neural Netw. 20 1204
[26]	Li Y, Qiang S, Zhuang X and Kaynak O 2004 IEEE Trans. Neural Netw. 15 693
[27]	Dawson D M, Carroll J J and Schneider M 1994 IEEE Trans. Control Syst. Technol. 2 233
[28]	Ioannou P A and Kokotovic P V 1984 Automatica 20 583
[29]	Wiggins S 1987 Phys. Lett. A 124 138
[30]	Hua C, Guan X and Shi P 2005 Chaos Soliton. Fract. 23 757
[31]	Levant A 2003 Int. J. Control 9 924
[32]	Khalil H K 1996 IEEE Trans. Auto. Contr. 41 177
[33]	Polycarpou M M 1996 IEEE Trans. Auto. Contr. 41 447
[34]	Sanner R M and Slotine J J 1992 IEEE Trans. Neur. Netw. 3 837
[35]	Wang H and Sun J 1992 Proceedings of the 31st IEEE Conference on Decision and Control 3255
[36]	Wen C, Zhou J, Liu Z and Su H 2011 IEEE Trans. Auto. Contr. 56 1672

Neural adaptive chaotic control with constrained input using state and output feedback

Neural adaptive chaotic control with constrained input using state and output feedback

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