Direct adaptive neural network control for switched reluctance motors with input saturation

2018 ◽  
Vol 13 (12) ◽  
pp. 1804-1814
Author(s):  
Cunhe Li ◽  
Guofeng Wang ◽  
Yan Li ◽  
Aide Xu
Keyword(s):  
Neural Network ◽  
Input Saturation ◽  
Network Control ◽  
Neural Network Control ◽  
Switched Reluctance Motors ◽  
Adaptive Neural Network ◽  
Switched Reluctance ◽  
Adaptive Neural Network Control

scholarly journals Adaptive Neural Network Control Scheme of Switched Systems with Input Saturation

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xiaoli Jiang ◽  
Mingyue Liu ◽  
Siqi Liu ◽  
Jing Xu ◽  
Lina Liu
Keyword(s):  
Neural Network ◽  
Tracking Error ◽  
Input Saturation ◽  
Small Neighborhood ◽  
Network Control ◽  
Neural Network Control ◽  
Adaptive Neural Network ◽  
Feedback Form ◽  
Design Algorithm ◽  
Adaptive Neural Network Control

This paper investigates a scheme of adaptive neural network control for a stochastic switched system with input saturation. The unknown smooth nonlinear functions are approximated directly by neural networks. A modified approach is proposed to deal with unknown functions with nonstrict feedback form in the design process. Furthermore, by combining the auxiliary design signal and the adaptive backstepping design, a valid adaptive neural tracking controller design algorithm is presented such that all the signals of the switched closed-loop system are in probability semiglobally, uniformly, and ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in probability. In the end, the effectiveness of the proposed method is verified by a simulation example.

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