Research on adaptive time-varying terminal sliding mode control

doi:10.3724/SP.J.1087.2013.00222

Journal of Computer Applications ›› 2013, Vol. 33 ›› Issue (01): 222-225.DOI: 10.3724/SP.J.1087.2013.00222

• Network and distributed techno • Previous Articles Next Articles

Research on adaptive time-varying terminal sliding mode control

HUANG Guoyong^1*,HU Jichen¹,WU Jiande¹,²,FAN Yugang¹,WANG Xiaodong¹,²

1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming Yunnan 650504, China
2. Engineering Research Center for Mineral Pipeline Transportation of Yunnan Province, Kunming Yunnan 650504, China

Received:2012-07-05 Revised:2012-08-09 Online:2013-01-09 Published:2013-01-01
Contact: HUANG Guoyong

自适应时变Terminal滑模控制研究

黄国勇^1*,胡吉晨¹,吴建德¹,²,范玉刚¹,王晓东¹,²

1. 昆明理工大学信息工程与自动化学院，昆明 650504
2. 云南省矿物管道输送工程技术研究中心，昆明 650500

通讯作者: 黄国勇
作者简介:黄国勇(1977-)，男，湖北天门人，副教授，博士，主要研究方向：模糊控制、滑模控制；胡吉晨(1988-)，男，安徽马鞍山人，硕士研究生，主要研究方向：故障诊断、机器学习；吴建德(1979-)，男，云南保山人，教授，博士，主要研究方向：智能控制、机器学习。
基金资助:
国家自然科学基金资助项目(51169007);云南省科技计划项目(2010DH004, 2011DA005);云南省中青年学术和技术带头人后备人才培养计划项目(2011CI017)

Abstract

Abstract: To resolve the problem of poor robustness when reaching the Terminal sliding mode control, a time-varying sliding mode control method was proposed. A nonlinear time-varying sliding mode surface was designed after analyzing the influences of designed parameters of sliding mode surface to the performances of system. To deal with the disturbances of a class of Multi-Input Multi-Output (MIMO) nonlinear system, a disturbance observer system was constructed. According to the disturbance observer system, the external disturbances were approached on-line by adjusting the weights. The simulation results show that, the settle-time of the proposed scheme is less than that of PID control by 80%. The proposed method has no overshoots. The simulation results demonstrate that the proposed design can be used on the control of MIMO nonlinear system.

Key words: time-varying sliding mode surface, disturbance observer system, Terminal sliding mode control, finite-time convergence

摘要： 针对Terminal滑模控制到达阶段鲁棒性不强的问题，提出了时变Terminal滑模控制方法。分析Terminal滑模面的设计参数对系统性能的影响，提出一种非线性时变Terminal滑模面的设计方法。为了消除多输入多输出(MIMO)非线性系统的不确定，构建动态干扰观测器系统，根据干扰观测误差在线调节参数，从而在线逼近外部干扰，证明了逼近误差一致最终有界。采用倒立摆系统进行仿真验证，提出的自适应时变Terminal滑模控制方法比传统的PID控制镇定时间缩短80%，且无超调。仿真结果表明，所提方法可以用于MIMO非线性系统的控制。

关键词: 时变滑模面, 干扰观测系统, Terminal滑模控制, 有限时间内收敛

CLC Number:

TP183
V249

HUANG Guoyong HU Jichen WU Jiande FAN Yugang WANG Xiaodong. Research on adaptive time-varying terminal sliding mode control[J]. Journal of Computer Applications, 2013, 33(01): 222-225.

黄国勇胡吉晨吴建德范玉刚王晓东. 自适应时变Terminal滑模控制研究[J]. 计算机应用, 2013, 33(01): 222-225.

References

［1］刘金琨，孙富春.滑模变结构控制理论及其算法研究与进展［J］.控制理论与应用，2007，24(3):407-418.

［2］MAN Z H, PAPLINSKI A P, WU H R, et al. A robust MIMO terminal sliding mode control scheme for rigid robotic manipulators ［J］. IEEE Transactions on Automatic Control, 1994, 39(12): 2464-2470.

［3］黄国勇.基于神经网络干扰观测器的Terminal滑模控制［J］.吉林大学学报:工学版，2011,41(6):1726-1730.

［4］张德江，张袅娜，冯勇.参数不确定机械手的快速终端滑模控制［J］.控制与决策，2010,25(3):433-436.

［5］李升波,李克强,王建强,等.非奇异快速的终端滑模控制方法及其跟车控制应用［J］.控制理论与应用，2010，27(5):543-550.

［6］赵霞，姜玉宪，吴云洁，等.基于多模态滑模的快速非奇异终端滑模控制［J］.北京航空航天大学学报，2011,37(1):100-103.

［7］池毓东，王岩，邵惠鹤.一种时变滑模平面的设计方法［J］.上海交通大学学报，1998,32(6):70-73.

［8］冯勇，安澄全，李涛.采用双滑模平面减小一类非线性系统稳态误差［J］.控制与决策，2000,15(3):361-365.

［9］胡庆雷,马广富,姜野,等.三轴稳定挠性卫星姿态机动时变滑模变结构和主动振动控制［J］.控制理论与应用，2009，26(2):122-126.

［10］WANG X Y, DENG Y G, WANG Y F, et al. Composite control of linear/adaptive TSM control for mass-controlled saucer-like air vehicle ［C］// Proceedings of the 6th World Congress on Intelligent Control and Automation. Boca Raton: CRC Press, 2006: 21-23.

［11］黄国勇.基于Terminal滑模的空天飞行器再入鲁棒自适应控制［D］.南京：南京航空航天大学，2007.

［12］黄国勇，姜长生，王玉惠.自适应Terminal滑模控制及其在UASV再入中的应用［J］.控制与决策，2007，22(11):1297-1301.

［13］YU S H, YU X H, MAN Z H. A fuzzy neural network approximator with fast terminal sliding mode and its applications ［J］. Fuzzy Sets and Systems, 2004, 148(3): 469-486.

[1]	SHAO Chao WAN Chunhong. Manifold learning and visualization based on self-organizing map [J]. Journal of Computer Applications, 2013, 33(07): 1917-1921.
[2]	DU Xiaoxin ZHANG Jianfei SUN Ming. Artificial glowworm swarm optimization algorithm based on adaptive t distribution mixed mutation [J]. Journal of Computer Applications, 2013, 33(07): 1922-1925.
[3]	GENG Huantong GAO Jun JIA Tingting WU Zhengxue. Multi-objective particle swarm optimization method with balanced diversity and convergence [J]. Journal of Computer Applications, 2013, 33(07): 1926-1929.
[4]	JIN Yuping DANG Jie. Boiler combustion efficiency optimization based on improved radial basis neural network [J]. Journal of Computer Applications, 2013, 33(06): 1771-1779.
[5]	YANG Shanxiu HAN Fei GUAN Jian. New gene selection method based on clustering and particle swarm optimization [J]. Journal of Computer Applications, 2013, 33(05): 1285-1288.
[6]	ZHANG Liwei YUAN Jinsha. Artificial immune algorithm based on intelligence complementary strategy [J]. Journal of Computer Applications, 2013, 33(04): 953-956.
[7]	ZHANG Yunong CHEN Junwei LIU Jinrong QU Lu LI Weibing. Unipolar Sigmoid neural network classifier based on weights and structure determination method [J]. Journal of Computer Applications, 2013, 33(03): 766-770.
[8]	HU Sheng LI Tai-fu WEI Zheng-yuan YAN Ke-sheng. Neural network for control chart pattern recognition based on kernel principle component analysis [J]. Journal of Computer Applications, 2012, 32(09): 2520-2522.
[9]	YI Zheng-jun HE Rong-hua HOU Kun. Quantum artificial bee colony optimization algorithm based on Bloch coordinates of quantum bit [J]. Journal of Computer Applications, 2012, 32(07): 1935-1938.
[10]	ZHOU Xiao-meng, XU Xiao-ming. Modified self-organizing map network for Euclidean travelling salesman problem [J]. Journal of Computer Applications, 2012, 32(07): 1962-1964.
[11]	ZHENG Bo-chuan SANG Yong-sheng. Lotka-Volterra recurrent neural network model for solving shortest path [J]. Journal of Computer Applications, 2012, 32(07): 1965-1968.
[12]	LIU Xiao-long ZHAO Kui-ling. Bacteria foraging optimization algorithm based on immune algorithm [J]. Journal of Computer Applications, 2012, 32(03): 634-637.
[13]	CHENG Hong-bing NI Shi-hong HUANG Guo-rong LIU Hua-wei JIANG Zheng-yong. Self-organizing fuzzy neural network algorithm based on unscented particle filter [J]. Journal of Computer Applications, 2011, 31(10): 2770-2773.
[14]	Jun-fang JIA. HC_AL: New active learning method based on hierarchical clustering [J]. Journal of Computer Applications, 2011, 31(08): 2134-2137.
[15]	Qi-chang DUAN Liang ZHANG Jing-ming YUAN. Fault diagnostic method for power converter based on wavelet neural network with improved algorithm [J]. Journal of Computer Applications, 2011, 31(08): 2143-2145.

Research on adaptive time-varying terminal sliding mode control

自适应时变Terminal滑模控制研究

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics