Fuzzy-approximation-based global adaptive control for uncertain strict-feedback systems with a priori known tracking accuracy

被引:61
作者
Wu, Jian [1 ]
Chen, Weisheng [1 ]
Li, Jing [1 ]
机构
[1] Xidian Univ, Sch Math & Stat, Xian 710071, Peoples R China
基金
中国国家自然科学基金;
关键词
Adaptive backstepping technique; Barbalat's Lemma; Fuzzy logic system; Global stability; Tracking accuracy; Uncertain strict-feedback system; MIMO NONLINEAR-SYSTEMS; BACKSTEPPING CONTROL; NEURAL-CONTROL; OUTPUT CONTROL; OBSERVER; DESIGN; STABILIZATION;
D O I
10.1016/j.fss.2014.10.009
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In this study, we propose a novel adaptive backstepping fuzzy control scheme for a class of uncertain strict-feedback systems where the tracking accuracy is known a priori, and we also introduce a multiswitching-based adaptive fuzzy controller. Compared with the existing method for adaptive fuzzy control, the advantages of the proposed scheme are as follows. First, the controller guarantees that all the closed-loop signals are globally uniformly ultimately bounded, which differs from most existing adaptive fuzzy control approaches where the semi-global boundedness of the closed-loop signals is ensured under a harsh assumption on the approximation domain of the fuzzy logic system. Second, our controller ensures that the tracking error converges to an accuracy that is given a priori for the uncertain strict-feedback system, which cannot be achieved using existing adaptive fuzzy control methods. Third, based on some nonnegative functions, we analyze the convergence of the tracking error using Barbalat's Lemma. Fourth, the main technical novelty is the construction of three new nth-order continuously differentiable switching functions, which are used to design the desired controller. Finally, three simulation examples are provided that illustrate the effectiveness of the proposed control strategy. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:1 / 25
页数:25
相关论文
共 49 条
[11]   Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems [J].
Ge, SS ;
Wang, J .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2002, 13 (06) :1409-1419
[12]   SYSTEMATIC DESIGN OF ADAPTIVE CONTROLLERS FOR FEEDBACK LINEARIZABLE SYSTEMS [J].
KANELLAKOPOULOS, I ;
KOKOTOVIC, PV ;
MORSE, AS .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1991, 36 (11) :1241-1253
[13]  
Khalil H., 2002, Nonlinear Systems, V3
[14]  
Lee T. H., 1998, Adaptive Neural Network Control of RoboticManipulators, V19
[15]   A Novel Robust Adaptive-Fuzzy-Tracking Control for a Class of Nonlinear Multi-Input/Multi-Output Systems [J].
Li, Tie-Shan ;
Tong, Shao-Cheng ;
Feng, Gang .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2010, 18 (01) :150-160
[16]   Adaptive Fuzzy Control for Synchronization of Nonlinear Teleoperators With Stochastic Time-Varying Communication Delays [J].
Li, Zhijun ;
Cao, Xiaoqing ;
Ding, Nan .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2011, 19 (04) :745-757
[17]   Adaptive fuzzy control for a class of uncertain nonaffine nonlinear systems [J].
Liu, Yan-Jun ;
Wang, Wei .
INFORMATION SCIENCES, 2007, 177 (18) :3901-3917
[18]   Adaptive Fuzzy Control via Observer Design for Uncertain Nonlinear Systems With Unmodeled Dynamics [J].
Liu, Yan-Jun ;
Tong, Shaocheng ;
Chen, C. L. Philip .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2013, 21 (02) :275-288
[19]   Adaptive Neural Output Feedback Controller Design with Reduced-Order Observer for a Class of Uncertain Nonlinear SISO Systems [J].
Liu, Yan-Jun ;
Tong, Shao-Cheng ;
Wang, Dan ;
Li, Tie-Shan ;
Chen, C. L. Philip .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2011, 22 (08) :1328-1334
[20]   Adaptive Neural Output Feedback Tracking Control for a Class of Uncertain Discrete-Time Nonlinear Systems [J].
Liu, Yan-Jun ;
Chen, C. L. Philip ;
Wen, Guo-Xing ;
Tong, Shaocheng .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2011, 22 (07) :1162-1167