Adaptive neural backstepping control of nonlinear fractional-order systems with input quantization

被引:1
作者
Cheng, Chao [1 ]
Wang, Huanqing [2 ]
Shen, Haikuo [1 ,4 ]
Liu, Peter X. [3 ]
机构
[1] Beijing Jiaotong Univ, Sch Mech Elect & Control Engn, Beijing, Peoples R China
[2] Bohai Univ, Sch Math, Jinzhou, Peoples R China
[3] Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON, Canada
[4] Beijing Jiaotong Univ, Sch Mech Elect & Control Engn, Beijing 100044, Peoples R China
基金
中国国家自然科学基金;
关键词
Adaptive control; fractional-order systems; RBF neural networks; input quantization; disturbance observer; DISTURBANCE-OBSERVER; UNCERTAIN SYSTEMS; STABILIZATION;
D O I
10.1177/01423312231155375
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 article addresses the tracking control problem of uncertain fractional-order nonlinear systems in the presence of input quantization and external disturbance. An adaptive backstepping scheme is proposed by combining with radial basis function (RBF) neural networks (NNs), fractional-order disturbance observer (FODO), and backstepping method. The RBF NNs are used to approximate the unknown nonlinearities of fractional-order systems. The FODO is designed to compensate for disturbance and uncertain parameters. The hysteresis quantizer is used to avoid chattering that possibly appears in actual application. The stability of the proposed controller is proved by fractional-order Lyapunov method. In addition, all the signals in the closed-loop system are bounded. The effectiveness of the proposed method is confirmed by the simulation results.
引用
收藏
页码:2848 / 2856
页数:9
相关论文
共 33 条
[1]   A Lyapunov-based control scheme for robust stabilization of fractional chaotic systems [J].
Aghababa, Mohammad Pourmahmood .
NONLINEAR DYNAMICS, 2014, 78 (03) :2129-2140
[2]   Lyapunov functions for fractional order systems [J].
Aguila-Camacho, Norelys ;
Duarte-Mermoud, Manuel A. ;
Gallegos, Javier A. .
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2014, 19 (09) :2951-2957
[3]   Fault tolerant control for robotic manipulator using fractional-order backstepping fast terminal sliding mode control [J].
Anjum, Zeeshan ;
Guo, Yu ;
Yao, Wei .
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2021, 43 (14) :3244-3254
[4]   Disturbance observer based control for nonlinear systems [J].
Chen, WH .
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2004, 9 (04) :706-710
[5]   A nonlinear disturbance observer for robotic manipulators [J].
Chen, WH ;
Ballance, DJ ;
Gawthrop, PJ ;
O'Reilly, J .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2000, 47 (04) :932-938
[6]  
Gorenflo R., 2014, Mittag-Leffler functions, related topics and applications, DOI DOI 10.1007/978-3-662-43930-2
[7]   Adaptive quantized control for nonlinear uncertain systems [J].
Hayakawa, Tomohisa ;
Ishii, Hideaki ;
Tsumura, Koji .
SYSTEMS & CONTROL LETTERS, 2009, 58 (09) :625-632
[8]   Dynamic measurements in long-memory materials: Fractional calculus evaluation of approach to steady state [J].
Heymans, Nicole .
JOURNAL OF VIBRATION AND CONTROL, 2008, 14 (9-10) :1587-1596
[9]   Output feedback NN tracking control for fractional-order nonlinear systems with time-delay and input quantization [J].
Hua, Changchun ;
Ning, Jinghua ;
Zhao, Guanglei ;
Li, Yafeng .
NEUROCOMPUTING, 2018, 290 :229-237
[10]   Design of fuzzy output feedback stabilization for uncertain fractional-order systems [J].
Ji, Yude ;
Su, Lianqing ;
Qiu, Jiqing .
NEUROCOMPUTING, 2016, 173 :1683-1693