Sliding mode control of neural networks via continuous or periodic sampling event-triggering algorithm

被引：68

作者：

Wang, Shiqin ^{[1
]}

Cao, Yuting ^{[2
]}

Huang, Tingwen ^{[3
]}

Chen, Yiran ^{[4
]}

Li, Peng ^{[5
]}

Wen, Shiping ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China

[2] Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China

[3] Texas A&M Univ Qatar, Sci Program, Doha 23874, Qatar

[4] Duke Univ, Dept Elect & Comp Engn, Durham, NC 27708 USA

[5] Univ Calif Santa Barbara, Dept Elect & Comp Engn, Santa Barbara, CA 93106 USA

来源：

NEURAL NETWORKS | 2020年 / 121卷

关键词：

Neural network; Sliding mode control; Event-triggering; Periodic sampling; VARIABLE-STRUCTURE SYSTEMS; STATE-FEEDBACK CONTROL; MEMRISTOR; SYNCHRONIZATION; PASSIVITY;

D O I：

10.1016/j.neunet.2019.09.001

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents the theoretical results on sliding mode control (SMC) of neural networks via continuous or periodic sampling event-triggered algorithm. Firstly, SMC with continuous sampling event-triggered scheme is developed and the practical sliding mode can be achieved. In addition, there is a consistent positive lower bound for the time interval between two successive trigger events which implies that the Zeno phenomenon will not occur. Next, a more economical and realistic SMC technique is presented with periodic sampling event-triggered algorithm, which guarantees the robust stability of the augmented system. Finally, two illustrative examples are presented to substantiate the effectiveness of the derived theoretical results. (C) 2019 Elsevier Ltd. All rights reserved.

引用

页码：140 / 147

页数：8

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