Prescribed performance synchronization for uncertain chaotic systems with input saturation based on neural networks

被引：21

作者：

Shao, Shuyi ^{[1
]}

Chen, Mou ^{[1
]}

Yan, Xiaohui ^{[1
]}

机构：

[1] Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing 211106, Jiangsu, Peoples R China

来源：

NEURAL COMPUTING & APPLICATIONS | 2018年 / 29卷 / 12期

关键词：

Chaotic systems; Adaptive neural network control; Synchronization control; Input saturation; ROBUST ADAPTIVE-CONTROL; DYNAMIC SURFACE CONTROL; MIMO NONLINEAR-SYSTEMS; NONHOLONOMIC MOBILE MANIPULATORS; ENERGY RESOURCE SYSTEM; PURE-FEEDBACK-SYSTEMS; UNKNOWN DEAD-ZONE; TRACKING CONTROL; SECURE COMMUNICATION; OBSERVER;

D O I：

10.1007/s00521-016-2629-5

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 paper, a prescribed performance adaptive neural network synchronization is investigated for a class of unknown chaotic systems in the presence of input saturation and external unknown disturbances. A prescribed performance function is employed to transform the constraint problem of chaotic synchronization control error into the problem of guaranteeing the boundedness of the transformed error. By introducing the Gaussian error function, the input saturation is handled. A neural network-based synchronization control scheme is then developed. Under the developed synchronization control scheme, the synchronization of uncertain chaotic systems is achieved with different initial conditions. Numerical simulation results further demonstrate the effectiveness of the proposed synchronization control scheme for unknown chaotic systems subject to external unknown disturbances and input saturation.

引用

页码：1349 / 1361

页数：13

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