Composite Adaptive Fuzzy Output Feedback Control Design for Uncertain Nonlinear Strict-Feedback Systems With Input Saturation

被引:395
作者
Li, Yongming [1 ]
Tong, Shaocheng [1 ]
Li, Tieshan [2 ]
机构
[1] Liaoning Univ Technol, Dept Math, Jinzhou 121001, Peoples R China
[2] Dalian Maritime Univ, Nav Coll, Dalian 116026, Peoples R China
基金
中国国家自然科学基金;
关键词
Composite adaptive fuzzy control; dynamic surface control (DSC); output-feedback control; serial-parallel estimation model; uncertain nonlinear systems; DYNAMIC SURFACE CONTROL; TRACKING CONTROL; NEURAL-CONTROL; BACKSTEPPING CONTROL;
D O I
10.1109/TCYB.2014.2370645
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, a composite adaptive fuzzy output-feedback control approach is proposed for a class of single-input and single-output strict-feedback nonlinear systems with unmeasured states and input saturation. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a fuzzy state observer is designed to estimate the unmeasured states. By utilizing the designed fuzzy state observer, a serial-parallel estimation model is established. Based on adaptive backstepping dynamic surface control technique and utilizing the prediction error between the system states observer model and the serial-parallel estimation model, a new fuzzy controller with the composite parameters adaptive laws are developed. It is proved that all the signals of the closed-loop system are bounded and the system output can follow the given bounded reference signal. A numerical example and simulation comparisons with previous control methods are provided to show the effectiveness of the proposed approach.
引用
收藏
页码:2299 / 2308
页数:10
相关论文
共 42 条
[1]  
[Anonymous], 2002, P I MECH ENG I-J SYS
[2]  
Bellomo D., 2005, P 16 IFAC WORLD C PR, P1093
[3]   Global asymptotic and robust stability of recurrent neural networks with time delays [J].
Cao, JD ;
Wang, J .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2005, 52 (02) :417-426
[4]   Fuzzy approximate disturbance decoupling of MIMO nonlinear systems by backstepping and application to chemical processes [J].
Chen, B ;
Liu, XP .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2005, 13 (06) :832-847
[5]   Adaptive fuzzy tracking control of nonlinear MIMO systems with time-varying delays [J].
Chen, Bing ;
Liu, Xiaoping ;
Liu, Kefu ;
Lin, Chong .
FUZZY SETS AND SYSTEMS, 2013, 217 :1-21
[6]   Direct adaptive fuzzy control of nonlinear strict-feedback systems [J].
Chen, Bing ;
Liu, Xiaoping ;
Liu, Kefu ;
Lin, Chong .
AUTOMATICA, 2009, 45 (06) :1530-1535
[7]   Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities [J].
Chen, Mou ;
Ge, Shuzhi Sam ;
How, Bernard Voon Ee .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2010, 21 (05) :796-812
[8]   Decentralized output-feedback neural control for systems with unknown interconnections [J].
Chen, Weisheng ;
Li, Junmin .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2008, 38 (01) :258-266
[9]   GLOBALLY DECENTRALIZED ADAPTIVE BACKSTEPPING NEURAL NETWORK TRACKING CONTROL FOR UNKNOWN NONLINEAR INTERCONNECTED SYSTEMS [J].
Chen, Weisheng ;
Li, Junmin .
ASIAN JOURNAL OF CONTROL, 2010, 12 (01) :96-102
[10]   Command Filtered Adaptive Backstepping [J].
Dong, Wenjie ;
Farrell, Jay A. ;
Polycarpou, Marios M. ;
Djapic, Vladimir ;
Sharma, Manu .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2012, 20 (03) :566-580