Implementation of a Fuzzy PID Controller Using Neural Network on the Magnetic Levitation System

2006 ◽  
Author(s):  
Agus Trisanto ◽  
Muhammad Yasser ◽  
Jianming Lu ◽  
Takashi Yahagi
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Magnetic Levitation ◽  
Fuzzy Pid ◽  
Fuzzy Pid Controller ◽  
Levitation System ◽  
Magnetic Levitation System

scholarly journals Artificial Neural Network vs PID Controller for Magnetic Levitation System

2020 ◽  
Vol 5 (7) ◽  
pp. 505-511
Author(s):  
G.M.K.B. Karunasena ◽  
H.D.N.S. Priyankara ◽  
B.G.D.A. Madhusank
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Pid Controller ◽  
Magnetic Levitation ◽  
Metal Sphere ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
Artificial Neural ◽  
Siso System

This research investigates the acceptability of the Artificial Neural Networks (ANN) over the PID Controller for the control of the Magnetic Levitation System (MLS). In the field of advanced control systems, this system identifies as a feedback-controlled, single input- single output (SISO) system. This SISO system used a PID controller for vertical trajectory controlling of a metal sphere in airspace by using an electromagnetic force that directed to upward. The vertical position of the metal sphere controlled according to the applied magnetic force generated by a powerful electromagnet and the electromagnetic force controlled by varying the supply voltage. To control this nonlinear system, we develop a multilayer artificial neural network by using Matlab software and integrate that with the physical magnetic levitation model. According to specific initial conditions, the actual responses of the magnetic levitation system with artificial neural network compares the desire response of the metal sphere. The ability of control this nonlinear system by using neural networks validate by comparing results obtained by the PID controller and artificial neural network.

Control of magnetic levitation system using recurrent neural network-based adaptive optimal backstepping strategy

2020 ◽  
Vol 42 (13) ◽  
pp. 2382-2395
Author(s):  
Armita Fatemimoghadam ◽  
Hamid Toshani ◽  
Mohammad Manthouri
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Optimization Problem ◽  
Closed Loop ◽  
Magnetic Levitation ◽  
Backstepping Control ◽  
Loop System ◽  
Closed Loop System ◽  
Levitation System ◽  
Magnetic Levitation System

In this paper, a novel approach is proposed for adjusting the position of a magnetic levitation system using projection recurrent neural network-based adaptive backstepping control (PRNN-ABC). The principles of designing magnetic levitation systems have widespread applications in the industry, including in the production of magnetic bearings and in maglev trains. Levitating a ball in space is carried out via the surrounding attracting or repelling magnetic forces. In such systems, the permissible range of the actuator is significant, especially in practical applications. In the proposed scheme, the procedure of designing the backstepping control laws based on the nonlinear state-space model is carried out first. Then, a constrained optimization problem is formed by defining a performance index and taking into account the control limits. To formulate the recurrent neural network (RNN), the optimization problem is first converted into a constrained quadratic programming (QP). Then, the dynamic model of the RNN is derived based on the Karush-Kuhn-Tucker (KKT) optimization conditions and the variational inequality theory. The convergence analysis of the neural network and the stability analysis of the closed-loop system are performed using the Lyapunov stability theory. The performance of the closed-loop system is assessed with respect to tracking error and control feasibility.

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