Adaptive quantized fuzzy control of stochastic nonlinear systems with actuator dead-zone

被引：48
作者：
Wang, Fang [1 ,2 ]
Liu, Zhi [1 ]
Zhang, Yun [1 ]
Chen, C. L. Philip [3 ]
机构：
[1] Guangdong Univ Technol, Fac Automat, Guangzhou, Guangdong, Peoples R China
[2] Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao, Peoples R China
[3] Univ Macau, Fac Sci & Technol, Zhuhai, Peoples R China
来源：
INFORMATION SCIENCES | 2016年 / 370卷
基金：
中国国家自然科学基金; 中国博士后科学基金; 国家教育部博士点专项基金资助;
关键词：
Adaptive fuzzy control; Backstepping technique; Unknown dead zone; Hysteretic quantizer; Stochastic nonlinear quantized systems; OUTPUT-FEEDBACK CONTROL; BACKSTEPPING CONTROL; UNCERTAIN SYSTEMS; TRACKING CONTROL; STABILIZATION; PLANTS;
D O I：
10.1016/j.ins.2016.07.070
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 studies a tracking issue of stochastic nonlinear quantized systems with actuator dead zone. By combing a sector-bounded property of a hysteretic quantizer and a simplified dead zone model, a novel connection between control signal and system input is established. Based on this connection, the stochastic nonlinear quantized control is transformed into the conventional stochastic nonlinear control with unknown control gain and bounded perturbation. Therefore, the control difficulty is overcome, which results from the coexistence of the unknown actuator dead zone and the quantization effect of the control signal. Then, fuzzy logic systems are utilized to cope with the unknown composite nonlinear functions including the bounded perturbation, and an adaptive learning mechanism is set up to compensate the unknown control gain. Hence, a refreshing adaptive fuzzy tracking control scheme is formed to achieve a desired tracking performance. (C) 2016 Elsevier Inc. All rights reserved.
引用
收藏
页码：385 / 401
页数：17
相关论文
共 61 条
[1]   Quantized feedback stabilization of linear systems [J].
Brockett, RW ;
Liberzon, D .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2000, 45 (07) :1279-1289
[2]   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
[3]   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
[4]   Direct adaptive fuzzy control of nonlinear strict-feedback systems [J].
Chen, Bing ;
Liu, Xiaoping ;
Liu, Kefu ;
Lin, Chong .
AUTOMATICA, 2009, 45 (06) :1530-1535
[5]  
Chen C., 2015, IEEE TRANS CONT SYST, DOI [10.1109/TCST.2015.2467205, DOI 10.1109/TCST.2015.2467205]
[6]   Saturated Nussbaum Function Based Approach for Robotic Systems With Unknown Actuator Dynamics [J].
Chen, Ci ;
Liu, Zhi ;
Zhang, Yun ;
Chen, C. L. Philip ;
Xie, Shengli .
IEEE TRANSACTIONS ON CYBERNETICS, 2016, 46 (10) :2311-2322
[7]   Stability of quantized time-delay nonlinear systems: a Lyapunov-Krasowskii-functional approach [J].
De Persis, Claudio ;
Mazenc, Frederic .
MATHEMATICS OF CONTROL SIGNALS AND SYSTEMS, 2010, 21 (04) :337-370
[8]   Robust stabilization of nonlinear systems by quantized and ternary control [J].
De Persis, Claudio .
SYSTEMS & CONTROL LETTERS, 2009, 58 (08) :602-608
[9]   An optimal quantized feedback strategy for scalar linear systems [J].
Delvenne, JC .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2006, 51 (02) :298-303
[10]   Stabilization of linear systems with limited information [J].
Elia, N ;
Mitter, SK .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2001, 46 (09) :1384-1400
1234567