A Synchronous Drive Control Scheme Based on Neural Networks for a Novel XY-table

2007 ◽  
Author(s):  
Jing Chen ◽  
Qiang Liu ◽  
Chang Qi
Keyword(s):  
Neural Networks ◽  
Drive Control ◽  
Control Scheme ◽  
Synchronous Drive

Nonfragile Dissipative Synchronization for Markovian Memristive Neural Networks: A Gain-Scheduled Control Scheme

2019 ◽  
Vol 30 (6) ◽  
pp. 1841-1853 ◽  
Author(s):  
Hao Shen ◽  
Ting Wang ◽  
Jinde Cao ◽  
Guoping Lu ◽  
Yongduan Song ◽  
...  
Keyword(s):  
Neural Networks ◽  
Memristive Neural Networks ◽  
Control Scheme ◽  
Gain Scheduled

scholarly journals Adaptive control scheme based on the least squares support vector machine network

2011 ◽  
Vol 21 (4) ◽  
pp. 685-696 ◽  
Author(s):  
Tarek Mahmoud
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Adaptive Control ◽  
Least Squares ◽  
Nonlinear System Identification ◽  
Support Vector ◽  
Reference Trajectory ◽  
Control Scheme ◽  
Support Vector Machine Network ◽  
Adaptive Control Scheme

Adaptive control scheme based on the least squares support vector machine networkRecently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine network (MRWLS-SVM) as a predictor model, and the controller part that is developed to track a reference trajectory. By means of the Lyapunov stability criterion, stability analysis for the tracking errors is performed. Finally, simulation studies are performed to demonstrate the capability of the developed approach in controlling a pH process.

Sign in / Sign up

Export Citation Format

Share Document