Adaptive Approximation-Based Tracking Control for a Class of Unknown High-Order Nonlinear Systems With Unknown Powers

被引:16
作者
Liu, Yong-Hua [1 ,2 ]
Liu, Yang [1 ,2 ]
Liu, Yu-Fa [1 ,2 ]
Su, Chun-Yi [1 ,2 ]
Zhou, Qi [1 ,2 ]
Lu, Renquan [1 ,2 ]
机构
[1] Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China
[2] Guangdong Univ Technol, Guangdong Prov Key Lab Intelligent Decis & Cooper, Guangzhou 510006, Peoples R China
基金
中国国家自然科学基金;
关键词
Nonlinear systems; Fuzzy logic; Control systems; Artificial neural networks; Adaptive control; Lyapunov methods; Adaptive approximation-based control; barrier Lyapunov function (BLF); global stability; high-order nonlinear systems; unknown powers; STRICT-FEEDBACK SYSTEMS; DYNAMIC SURFACE CONTROL; BARRIER LYAPUNOV FUNCTIONS; ASYMPTOTIC TRACKING; STABILIZATION; INTEGRATOR;
D O I
10.1109/TCYB.2020.3030310
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 article, the problem of adaptive tracking control is tackled for a class of high-order nonlinear systems. In contrast to existing results, the considered system contains not only unknown nonlinear functions but also unknown rational powers. By utilizing the fuzzy approximation approach together with the barrier Lyapunov functions (BLFs), we present a new adaptive tracking control strategy. Remarkably, the BLFs are employed to determine a priori the compact set for maintaining the validity of fuzzy approximation. The primary advantage of this article is that the developed controller is independent of the powers and can be capable of ensuring global stability. Finally, two illustrative examples are given to verify the effectiveness of the theoretical findings.
引用
收藏
页码:4559 / 4573
页数:15
相关论文
共 50 条
[1]   Control Barrier Function Based Quadratic Programs for Safety Critical Systems [J].
Ames, Aaron D. ;
Xu, Xiangru ;
Grizzle, Jessy W. ;
Tabuada, Paulo .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2017, 62 (08) :3861-3876
[2]   Nonlinear dynamics of two degree of freedom systems with linear and nonlinear stiffnesses [J].
Bayat, M. ;
Pakar, I. .
EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION, 2013, 12 (03) :411-420
[3]   Fuzzy-Approximation-Based Adaptive Control of Strict-Feedback Nonlinear Systems With Time Delays [J].
Chen, Bing ;
Liu, Xiaoping ;
Liu, Kefu ;
Lin, Chong .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2010, 18 (05) :883-892
[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]   Globally Stable Adaptive Backstepping Neural Network Control for Uncertain Strict-Feedback Systems With Tracking Accuracy Known a Priori [J].
Chen, Weisheng ;
Ge, Shuzhi Sam ;
Wu, Jian ;
Gong, Maoguo .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 26 (09) :1842-1854
[6]   Adaptive Finite-Time Stabilization of a Class of Uncertain Nonlinear Systems via Logic-Based Switchings [J].
Fu, Jun ;
Ma, Ruicheng ;
Chai, Tianyou .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2017, 62 (11) :5998-6003
[7]   Modeling and Compensation of Asymmetric Hysteresis Nonlinearity for Piezoceramic Actuators With a Modified Prandtl-Ishlinskii Model [J].
Gu, Guo-Ying ;
Zhu, Li-Min ;
Su, Chun-Yi .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2014, 61 (03) :1583-1595
[8]   Adaptive control of a class of nonlinear systems with nonlinearly parameterized fuzzy approximators [J].
Han, H ;
Su, CY ;
Stepanenko, Y .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2001, 9 (02) :315-323
[9]  
Hardy G. H., 1967, Inequalities
[10]   Global adaptive neural dynamic surface control of strict-feedback systems [J].
Huang, Jeng-Tze .
NEUROCOMPUTING, 2015, 165 :403-413