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

Obed, Adel A.; Saleh, Ameer L.; Kadhim, Abbas

doi:10.11591/ijpeds.v10.i4.pp1742-1750

Cited by 17 publications

(15 citation statements)

References 8 publications

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“…c, I and a are explained by (14). When the butterfly can sense the smell emanating from another butterfly, it will move towards it, and this stage is known as the global search and can be represented by (15), and when the butterfly is unable to sense the smell emanating from any other butterfly, it will move randomly and this stage is called the local search and It can be represented by (16).…”

Section: Butterfly Optimization Algorithm (Boa)mentioning

confidence: 99%

“…The purpose of using the cascade control unit is due to several reasons, the most important of which is to reduce or reject disturbance and return to the steady-state [10]- [12]. There are several methods to tuning the parameters of PID and use it in PMDC motor such as Ziegler-Nichols Method (Z-N), Cohen-Coon method, artificial neural, network fuzzy logic [13], [14], particle swarm optimization (PSO) [15], [16], genetic algorithm [17], and butterfly optimization algorithm (BOA) [18].…”

Section: Introductionmentioning

confidence: 99%

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Comparison between butterfly optimization algorithm and particle swarm optimization for tuning cascade PID control system of PMDC motor

Abdulhussein

Yasin

Hasan

2021

IJPEDS

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In this paper, two optimization methods are used to adjust the gain values for the cascade PID controller. These algorithms are the butterfly optimization algorithm (BOA), which is a modern method based on tracking the movement of butterflies to the scent of a fragrance to reach the best position and the second method is particle swarm optimization (PSO). The PID controllers in this system are used to control the position, velocity, and current of a permanent magnet DC motor (PMDC) with an accurate tracking trajectory to reach the desired position. The simulation results using the Matlab environment showed that the butterfly optimization algorithm is better than the particle swarming optimization (PSO) in terms of performance and overshoot or any deviation in tracking the path to reach the desired position. While an overshoot of 2.557% was observed when using the PSO algorithm, and a position deviation of 7.82 degrees was observed from the reference position.

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Section: Butterfly Optimization Algorithm (Boa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison between butterfly optimization algorithm and particle swarm optimization for tuning cascade PID control system of PMDC motor

Abdulhussein

Yasin

Hasan

2021

IJPEDS

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“…Figure 3 depicts the structure of recurrent wavelet network. Hence, the output for each layer can be calculated as [20,23,24]:…”

Section: Recurrent Wavelet Neural Network (Rwnns)mentioning

confidence: 99%

“…The WNN-PID controller based on PSO is proposed in this section, which combines the ability of the artificial neural networks for learning with the ability of wavelet for identification, control of dynamic system, and also having the capability of self-learning and adapting [10,11,19,37]. Two types of wavelet network are modified in this section, feedforward WNN and proposed recurrent WNN with online tuning optimization using PSO algorithm [22][23][24].…”

Section: Speed Control Of Bldc Motor Based On Wavelet Neural Networkmentioning

confidence: 99%

Wavelet Neural Networks for Speed Control of BLDC Motor

Saleh¹,

Obed²,

Qasim³

et al. 2021

Automation and Control

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In the recent years, researchers have sophisticated the synthesis of neural networks depending on the wavelet functions to build the wavelet neural networks (WNNs), where the wavelet function is utilized in the hidden layer as a sigmoid function instead of conventional sigmoid function that is utilized in artificial neural network. The WNN inherits the features of the wavelet function and the neural network (NN), such as self-learning, self-adapting, time-frequency location, robustness, and nonlinearity. Besides, the wavelet function theory guarantees that the WNN can simulate the nonlinear system precisely and rapidly. In this chapter, the WNN is used with PID controller to make a developed controller named WNN-PID controller. This controller will be utilized to control the speed of Brushless DC (BLDC) motor to get preferable performance than the traditional controller techniques. Besides, the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of the WNN-PID controller. The modification for this method of the WNN such as the recurrent wavelet neural network (RWNN) was included in this chapter. Simulation results for all the above methods are given and compared.

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“…The weakness of PID is to achieve high performance from a controller that has required accurate and precise control parameters. A good PID control setting will have an impact on optimal system response [3]. It is really depending on the parameter settings and the mathematical model.…”

Section: Introductionmentioning

confidence: 99%

Improving neural network using a sine tree-seed algorithm for tuning motor DC

Aribowo

Suprianto

Joko

2021

IJPEDS

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A DC motor is applied to delicate speed and position in the industry. The stability and productivity of a system are keys for tuning of a DC motor speed. Stabilized speed is influenced by load sway and environmental factors. In this paper, a comparison study in diverse techniques to tune the speed of the DC motor with parameter uncertainties is showed. The research has discussed the application of the feed-forward neural network (FFNN) which is enhanced by a sine tree-seed algorithm (STSA). STSA is a hybrid method of the tree-seed algorithm (TSA) and Sine Cosine algorithm. The STSA method is aimed to improve TSA performance based on the sine cosine algorithm (SCA) method. A feed-forward neural network (FFNN) is popular and capable of nonlinear issues. The focus of the research is on the achievement speed of DC motor. In addition, the proposed method will be compared with proportional integral derivative (PID), FFNN, marine predator algorithm-feed-forward neural network (MPA-NN) and atom search algorithm-feed-forward neural network (ASO-NN). The performance of the speed from the proposed method has the best result. The settling time value of the proposed method is more stable than the PID method. The ITAE value of the STSA-NN method was 31.3% better than the PID method. Meanwhile, the ITSE value is 29.2% better than the PID method.

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

Cited by 17 publications

References 8 publications

Comparison between butterfly optimization algorithm and particle swarm optimization for tuning cascade PID control system of PMDC motor

Comparison between butterfly optimization algorithm and particle swarm optimization for tuning cascade PID control system of PMDC motor

Wavelet Neural Networks for Speed Control of BLDC Motor

Improving neural network using a sine tree-seed algorithm for tuning motor DC

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