Adaptive Fuzzy Prescribed Performance Control of Nontriangular Structure Nonlinear Systems

被引:158
作者
Li, Yongming [1 ,2 ]
Shao, Xinfeng [1 ]
Tong, Shaocheng [1 ,2 ]
机构
[1] Liaoning Univ Technol, Coll Sci, 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
Nonlinear systems; Adaptive systems; Control design; Backstepping; Fuzzy logic; Process control; Adaptive fuzzy control; backstepping design technique; nontriangular structure nonlinear systems; prescribed performance control; DYNAMIC SURFACE CONTROL; OUTPUT-FEEDBACK CONTROL; NEURAL-NETWORK CONTROL; CONTROL DESIGN;
D O I
10.1109/TFUZZ.2019.2937046
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this article, a new n-step fuzzy adaptive output tracking prescribed performance control problem is investigated for a class of nontriangular structure nonlinear systems. In the control design process, the mean value theorem is used to separate the virtual state variables needed for the control design, and the implicit function theorem is exploited to assert the existence of the desired continuous control. The fuzzy logic systems are used to identify the unknown nonlinear functions and ideal controller, respectively. By constructing a novel iterative Lyapunov function, a new n-step adaptive backstepping control design algorithm is established. The prominent characteristics of the proposed adaptive fuzzy backstepping control design algorithm are as follows: one is that it can ensure the closed-loop control system is the semiglobally uniformly ultimately bounded and the tracking error can converge within the prescribed performance bounds. The other is that it solves the controller design problem for the nontriangular nonlinear systems that the previous adaptive backstepping design techniques cannot deal with. Two examples are provided to show the effectiveness of the presented control method.
引用
收藏
页码:2416 / 2426
页数:11
相关论文
共 35 条
[1]   Decentralized Adaptive Fuzzy Secure Control for Nonlinear Uncertain Interconnected Systems Against Intermittent DoS Attacks [J].
An, Liwei ;
Yang, Guang-Hong .
IEEE TRANSACTIONS ON CYBERNETICS, 2019, 49 (03) :827-838
[2]   Approximation-Based Adaptive Neural Control Design for a Class of Nonlinear Systems [J].
Chen, Bing ;
Liu, Kefu ;
Liu, Xiaoping ;
Shi, Peng ;
Lin, Chong ;
Zhang, Huaguang .
IEEE TRANSACTIONS ON CYBERNETICS, 2014, 44 (05) :610-619
[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]   Asymptotic Fuzzy Neural Network Control for Pure-Feedback Stochastic Systems Based on a Semi-Nussbaum Function Technique [J].
Chen, Ci ;
Liu, Zhi ;
Xie, Kan ;
Zhang, Yun ;
Chen, C. L. Philip .
IEEE TRANSACTIONS ON CYBERNETICS, 2017, 47 (09) :2448-2459
[5]   Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints [J].
Chen, Mou ;
Ge, Shuzhi Sam ;
Ren, Beibei .
AUTOMATICA, 2011, 47 (03) :452-465
[6]   Minimal-Approximation-Based Decentralized Backstepping Control of Interconnected Time-Delay Systems [J].
Choi, Yun Ho ;
Yoo, Sung Jin .
IEEE TRANSACTIONS ON CYBERNETICS, 2016, 46 (12) :3401-3413
[7]   Integrator backstepping control of a brush DC motor turning a robotic load [J].
Dawson, D.M. ;
Carroll, J.J. ;
Schneider, M. .
IEEE Transactions on Control Systems Technology, 1994, 2 (03) :233-244
[8]   Adaptive neural network control for a class of low-triangular-structured nonlinear systems [J].
Du, HB ;
Shao, HH ;
Yao, PJ .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2006, 17 (02) :509-514
[9]   Adaptive NN control of uncertain nonlinear pure-feedback systems [J].
Ge, SS ;
Wang, C .
AUTOMATICA, 2002, 38 (04) :671-682
[10]   Partial Tracking Error Constrained Fuzzy Dynamic Surface Control for a Strict Feedback Nonlinear Dynamic System [J].
Han, Seong I. ;
Lee, Jang M. .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2014, 22 (05) :1049-1061