Lyapunov-based switching control using neural networks for a remotely operated vehicle

被引：16
作者：
Cavalletti, M. [1 ]
Ippoliti, G. [1 ]
Longhi, S. [1 ]
机构：
[1] Univ Politecn Marche, Dipartimento Ingn Informat Gestionale & Automaz, I-60131 Ancona, Italy
来源：
INTERNATIONAL JOURNAL OF CONTROL | 2007年 / 80卷 / 07期
关键词：
D O I：
10.1080/00207170701222939
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper considers the tracking control problem of an underwater vehicle subjected to different load configurations, which from time to time introduce considerable variations of its mass and inertial parameters. The control of this kind of mode-switch process cannot be adequately faced with traditional adaptive control techniques because of the too long time needed for adaptation. To cope with this problem, a switching control scheme is proposed and the stability of this multi-controller system is analysed using the Lyapunov theory. The performance of the switched controller is evaluated by numerical simulations.
引用
收藏
页码：1077 / 1091
页数：15
相关论文
共 55 条
[21]   Controller switching based on output prediction errors [J].
Hocherman-Frommer, J ;
Kulkarni, SR ;
Ramadge, PJ .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1998, 43 (05) :596-607
[22]   NEURAL NETWORKS FOR CONTROL-SYSTEMS - A SURVEY [J].
HUNT, KJ ;
SBARBARO, D ;
ZBIKOWSKI, R ;
GAWTHROP, PJ .
AUTOMATICA, 1992, 28 (06) :1083-1112
[23]   Progress in supervised neural networks [J].
Hush, Don R. ;
Horne, Bill G. .
IEEE SIGNAL PROCESSING MAGAZINE, 1993, 10 (01) :8-39
[24]   Multiple models for adaptive control to improve the performance of minimum variance regulators [J].
Ippoliti, G ;
Longhi, S .
IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS, 2004, 151 (02) :210-217
[25]  
Ippoliti G., 2005, J MARINE ENG TECHNOL, V4, P3
[26]  
Isidori A., 1989, NONLINEAR CONTROL SY
[27]  
Karimi A, 2001, INT J ADAPT CONTROL, V15, P335, DOI 10.1002/acs.670
[28]  
Kosut R. L., 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251), P566, DOI 10.1109/ACC.1999.782891
[29]   Adaptive control for uncertain nonlinear systems based on multiple neural networks [J].
Lee, CY ;
Lee, JJ .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2004, 34 (01) :325-333
[30]   An adaptive tracking controller using neural networks for a class of nonlinear systems [J].
Man, ZH ;
Wu, HR ;
Palaniswami, M .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1998, 9 (05) :947-955
123456