An Online Trained Adaptive Neural Network Controller for an Active Magnetic Bearing System

2014 ◽  
Author(s):  
Seng Chi Chen ◽  
Van Sum Nguyen ◽  
Dinh Kha Le ◽  
Nguyen Thi Hoai Nam
Keyword(s):  
Neural Network ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
Adaptive Neural Network ◽  
Neural Network Controller ◽  
Network Controller ◽  
Bearing System

scholarly journals APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN THE CONTEXT OF ACTIVE MAGNETIC BEARING CONTROL SYSTEMS

2021 ◽  
Vol 3 ◽  
pp. 159-162
Author(s):  
Alexander Kravtsov ◽  
Konstantin Vukolov ◽  
Igor Plokhov ◽  
Igor Savraev ◽  
Sergei Loginov
Keyword(s):  
Neural Network ◽  
Magnetic Bearing ◽  
Magnetic Suspension ◽  
Active Magnetic Bearing ◽  
Rolling Moment ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Network Methods ◽  
Single Mass

The article is devoted to the application of neural network methods and genetic algorithms in solving problems of controlling an electric drive of an active magnetic suspension. The method of rolling moment for eliminating an imbalance is considered. The scheme of the neural network controller and the curves of the transients in the open single-mass electromechanical system and in the system c of the neurocontrollers are presented.

scholarly journals Adaptive Neural Network Control with Backstepping for Surface Ships with Input Dead-Zone

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Guoqing Xia ◽  
Xingchao Shao ◽  
Ang Zhao ◽  
Huiyong Wu
Keyword(s):  
Neural Network ◽  
Explicit Knowledge ◽  
Dead Zone ◽  
Nonlinear Function ◽  
Lyapunov Theory ◽  
Adaptive Neural Network ◽  
Neural Network Controller ◽  
Network Controller ◽  
Adaptive Neural Network Control ◽  
Fuzzy Logic Technique

This paper addresses the problem of adaptive neural network controller with backstepping technique for fully actuated surface vessels with input dead-zone. The combination of approximation-based adaptive technique and neural network system is used for approximating the nonlinear function of the ship plant. Through backstepping and Lyapunov theory synthesis, an indirect adaptive network controller is derived for dynamic positioning ships without dead-zone property. In order to improve the control effect, a dead-zone compensator is derived using fuzzy logic technique to handle the dead-zone nonlinearity. The main advantage of the proposed controller is that it can be designed without explicit knowledge about the ship motion model, and dead-zone nonlinearity is well compensated. A set of simulations is carried out to verify the performance of the proposed controller.

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