Adaptive Fuzzy State/Output Feedback Control of Nonstrict-Feedback Systems: A Direct Compensation Approach

被引:11
作者
Huang, Jeng-Tze [1 ]
机构
[1] Chinese Culture Univ, Inst Digital Mechatron Technol, Taipei 11114, Taiwan
关键词
Adaptive fuzzy; direct compensation; dynamic surface control; multiswitching; nonstrict-feedback; scaling transformation; CANONICAL NONLINEAR-SYSTEMS; DYNAMIC SURFACE CONTROL; DISCRETE-TIME-SYSTEMS; HIGH-GAIN OBSERVERS; TRACKING CONTROL; STATE ESTIMATION; DEAD ZONE; APPROXIMATION; STABILIZATION; DIFFERENTIATION;
D O I
10.1109/TCYB.2018.2818791
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Issues of adaptive fuzzy direct compensation-based state/output feedback control for nonstrict-feedback systems are presented. The key to its feasibility is the differentiation-free feature, which is achieved in two steps. First, with the nominal adaptive fuzzy virtual controllers as the inputs, a set of low-pass filters are constructed to avoid the explosion of complexity and the algebraic-loop problems. Second, via using sufficiently small time constants, the boundedness of the filters' errors is ensured without the calculation of the filter error dynamics, which otherwise would incur another loop problem. In particular, by including a supervisory linear high-gain control component, both the state/output feedback control schemes ensure the semi-global practical tracking stability without relying on the all-time validity of the fuzzy approximation. In particular, the stability criteria of the proposed state and output feedback designs are much easier to fulfill than those based on the variable-separation-based method. Simulation are then carried out to validate the proposed schemes.
引用
收藏
页码:2046 / 2059
页数:14
相关论文
共 72 条
[1]   High-gain observers in the presence of measurement noise: A switched-gain approach [J].
Ahrens, Jeffrey H. ;
Khalil, Hassan K. .
AUTOMATICA, 2009, 45 (04) :936-943
[2]  
[Anonymous], 2006, NONLINEAR SYSTEMS
[3]   Observer-Based Adaptive Fuzzy Control for a Class of Nonlinear Delayed Systems [J].
Chen, Bing ;
Lin, Chong ;
Liu, Xiaoping ;
Liu, Kefu .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2016, 46 (01) :27-36
[4]   Observer-Based Adaptive Neural Network Control for Nonlinear Systems in Nonstrict-Feedback Form [J].
Chen, Bing ;
Zhang, Huaguang ;
Lin, Chong .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2016, 27 (01) :89-98
[5]   Fuzzy Approximation-Based Adaptive Control of Nonlinear Delayed Systems With Unknown Dead Zone [J].
Chen, Bing ;
Liu, Xiaoping ;
Liu, Kefu ;
Lin, Chong .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2014, 22 (02) :237-248
[6]   Adaptive Fuzzy Control of a Class of Nonlinear Systems by Fuzzy Approximation Approach [J].
Chen, Bing ;
Liu, Xiaoping P. ;
Ge, Shuzhi Sam ;
Lin, Chong .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2012, 20 (06) :1012-1021
[7]   Direct adaptive fuzzy control of nonlinear strict-feedback systems [J].
Chen, Bing ;
Liu, Xiaoping ;
Liu, Kefu ;
Lin, Chong .
AUTOMATICA, 2009, 45 (06) :1530-1535
[8]  
Chen C.T., 1995, Linear System Theory and Design
[9]   An Asynchronous Operation Approach to Event-Triggered Control for Fuzzy Markovian Jump Systems With General Switching Policies [J].
Cheng, Jun ;
Park, Ju H. ;
Zhang, Lixian ;
Zhu, Yanzheng .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018, 26 (01) :6-18
[10]   Finite-time H∞ fuzzy control of nonlinear Markovian jump delayed systems with partly uncertain transition descriptions [J].
Cheng, Jun ;
Park, Ju H. ;
Liu, Yajuan ;
Liu, Zhijun ;
Tang, Liming .
FUZZY SETS AND SYSTEMS, 2017, 314 :99-115