scholarly journals Speed Control of BLDC Motor Based on Recurrent Wavelet Neural Network

2014 ◽  
Vol 10 (2) ◽  
pp. 118-129
Author(s):  
Adel Obed ◽  
Ameer Saleh
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Pso Algorithm ◽  
Wavelet Neural Network ◽  
Motor Drive ◽  
Bldc Motor ◽  
Wide Range ◽  
Stator Resistance ◽  
And Control ◽  
Self Learning

In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN) is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learning from the BLDC motor drive and the capability of wavelet decomposition for identification and control of dynamic system and also having the ability of self-learning and self-adapting. The proposed controller is applied for controlling the speed of BLDC motor which provides a better performance than using conventional controllers with a wide range of speed. The parameters of the proposed controller are optimized using Particle Swarm Optimization (PSO) algorithm. The BLDC motor drive with RWNN-PID controller through simulation results proves a better in the performance and stability compared with using conventional PID and classical WNN-PID controllers.

scholarly journals Speed performance evaluation of BLDC motor based on dynamic wavelet neural network and PSO algorithm

2019 ◽  
Vol 10 (4) ◽  
pp. 1742 ◽  
Author(s):  
Adel Ahmed Obed ◽  
Ameer Lateef Saleh ◽  
Abbas Kareem Kadhim
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Pso Algorithm ◽  
Wavelet Neural Network ◽  
Superior Performance ◽  
Bldc Motor ◽  
Motor Speed ◽  
Brushless Dc ◽  
Wide Range ◽  
Simulation Results

<span>In this paper, several methods are developed to control the brushless DC (BLDC) motor speed. Since it is difficult to get a good showing by utilizing classical PID controller, the Dynamic Wavelet Neural Network (DWNN) is the proposed work in this paper, with parallel PID controller to obtain an novel controller named DWNN-PID controller. It collects the artificial neural ability of its networks for imparting from motor of BLDC with drive system and the ability of identification for the wavelet decomposition and control of the dynamic system furthermore to have ability for adapting and self-learning.  The suggested controller method is utilizing to control the speed of BLDC motor of which supply a better showing than utilizing classical controllers with a wide range of control. The proposed controller parameters are matched continuously using Particle Swarm Optimization (PSO) algorithm. The simulation results based on proposed DWNN-PID controller demonstrate a superior in the stability and performance compared at utilizing classical WNN-PID and conventional PID controllers. The simulation results are accomplished using Matlab/Simulink. It shows that the proposed control scheme has a superior performance.</span>

scholarly journals Motion control of linear induction motor based on optimal recurrent wavelet neural network-PID controller

2018 ◽  
Vol 7 (4) ◽  
pp. 2028 ◽  
Author(s):  
Ameer L. Saleh ◽  
Badiryah A. Obaid ◽  
Adel A. Obed
Keyword(s):  
Neural Network ◽  
Induction Motor ◽  
Pid Controller ◽  
Pulse Width Modulation ◽  
Pso Algorithm ◽  
Wavelet Neural Network ◽  
Voltage Source ◽  
End Effects ◽  
Linear Induction ◽  
Linear Induction Motor

This paper contains a proposed controller based on optimal recurrent wavelet neural network (RWNN) with PID controller to control the velocity and hence the stator current as well as the developed thrust of three phase linear induction motor (LIM) which consider the end effects. A vector control represented by indirect field oriented control (IFOC) technique is appointed to attain velocity and flux control for different loading conditions. Moreover, a voltage source inverter based on space vector pulse width modulation (SVPWM) is utilized to give the required stator voltage of LIM. A well-known particle swarm optimization (PSO) algorithm is employed for online tuning of the proposed controller. The computer simulation results show that this controller is effective and gives preferable and rigorous performance compared with a results obtained from conventional wavelet neural network (WNN) and PID controllers.  

scholarly journals Single-Phase Photovoltaic Grid-Connected Inverter Based on Fuzzy Neural Network

2021 ◽  
Vol 25 (3) ◽  
pp. 310-316
Author(s):  
Shenping Xiao ◽  
Zhouquan Ou ◽  
Junming Peng ◽  
Yang Zhang ◽  
Xiaohu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Single Phase ◽  
Fuzzy Inference ◽  
Learning Ability ◽  
Proportional Integral ◽  
Fuzzy Neural ◽  
Grid Connected Inverter ◽  
Self Learning

Based on a single-phase photovoltaic grid-connected inverter, a control strategy combining traditional proportional–integral–derivative (PID) control and a dynamic optimal control algorithm with a fuzzy neural network was proposed to improve the dynamic characteristics of grid-connected inverter systems effectively. A fuzzy inference rule was established after analyzing the proportional, integral, and differential coefficients of the PID controller. A fuzzy neural network was applied to adjust the parameters of the PID controller automatically. Accordingly, the proposed dynamic optimization algorithm was deduced in theory. The simulation and experimental results showed that the method was effective in making the system more robust to external disruption owing to its excellent steady-state adaptivity and self-learning ability.

WNN Model Based on Particle Swarm Optimization for Fault Diagnosis in Analog Circuit

2013 ◽  
Vol 427-429 ◽  
pp. 1048-1051
Author(s):  
Xu Sheng Gan ◽  
Hao Lin Cui ◽  
Ya Rong Wu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Convergence Rate ◽  
Analog Circuit ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Wavelet Neural Network ◽  
Swarm Optimization ◽  
Fast Convergence Rate

In order to diagnose the fault in analog circuit correctly, a Wavelet Neural Network (WNN) method is proposed that uses the Particle Swarm Optimization (PSO) algorithm to optimize the network parameters. For the improvement of convergence rate in WNN based on PSO algorithm, a compressing method in research space is introduced into the traditional PSO algorithm to improve the convergence in WNN training. The simulation shows that the proposed method has a good diagnosis with fast convergence rate for the fault in analog circuit.

Research on Neural Network Control Technology for Satellite-Based Laser Communication Tracking Turntable

2014 ◽  
Vol 945-949 ◽  
pp. 2266-2271
Author(s):  
Li Hua Wang ◽  
Xiao Qiang Wu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Mathematic Model ◽  
Network Control ◽  
Learning Ability ◽  
Space Environment ◽  
Laser Communication ◽  
Self Learning

In space laser communication tracking turntable work environment characteristics, we design a neural network PID control system which makes the system’s parameter self-tuning. The control system cans self-tune parameters under the changes of the object’ mathematic model, it solves the problem for the control object’s model changes under the space environment. It also looks for method for optimum control through the function of neural network's self-learning in order to solve the problem of the precision’s decline which arouse from vibration and disturbance. The simulation experiments confirmed the self-learning ability of neural network, and described the neural PID controller dynamic performance is superior to the classical PID controller through the output characteristic curves contrast.

scholarly journals Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4946
Author(s):  
Tuan Pham Van ◽  
Dung Vo Tien ◽  
Zbigniew Leonowicz ◽  
Michal Jasinski ◽  
Tomasz Sikorski ◽  
...  
Keyword(s):  
Neural Network ◽  
Induction Motor ◽  
Learning Rate ◽  
Accurate Estimation ◽  
Motor Drive ◽  
Induction Motor Drive ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Artificial Neural ◽  
Stator Resistance

This paper presents a new approach method for online rotor and stator resistance estimation of induction motors using artificial neural networks for the sensorless drive. In this method, the rotor resistance is estimated by a feed-forward neural network with the learning rate as a function. The stator resistance is also estimated using the two-layered neural network with learning rate as a function. The speed of the induction motor is also estimated by the neural network. Therefore, the accurate estimation of the rotor and stator resistance improved the quality of the sensorless induction motor drive. The results of simulation and experiment show that the estimated speed tracks the real speed of the induction motor; simultaneously, the error between the estimated rotor and stator resistance using neural network and the normal rotor and stator resistance is very small.

Sign in / Sign up

Export Citation Format

Share Document