Chaotic inertia weight and constriction factor-based PSO algorithm for BLDC motor drive control

Merugumalla, Manoj Kumar; Navuri, Prema Kumar

doi:10.1504/ijpse.2019.096673

Cited by 5 publications

(2 citation statements)

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“…The range of the population size is generally from 20 to 50. If ω = 0, then the adjustment of particle velocity is related only to the current position and the historical best position, and the change in velocity size has nothing to do with it [24]. When the inertia factor ω is added, particles can effectively explore other parts of space, not only in the vicinity of the individual and global best positions but also in the function of global and local exploration.…”

Section: Construction Of Prediction Model Of Crop Water and Nitrogen ...mentioning

confidence: 99%

Enhanced Neural Network for Rapid Identification of Crop Water and Nitrogen Content Using Multispectral Imaging

Peng,

He,

Zheng

et al. 2023

Agronomy

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Precision irrigation and fertilization in agriculture are vital for sustainable crop production, relying on accurate determination of the crop’s nutritional status. However, there are challenges in optimizing traditional neural networks to achieve this accurately. This paper aims to propose a rapid identification method for crop water and nitrogen content using optimized neural networks. This method addresses the difficulty in optimizing the traditional backpropagation neural network (BPNN) structure. It uses 179 multi−spectral images of crops (such as maize) as samples for the neural network model. Particle swarm optimization (PSO) is applied to optimize the hidden layer nodes. Additionally, this paper proposes a double−hidden−layer network structure to improve the model’s prediction accuracy. The proposed double−hidden−layer PSO−BPNN model showed a 9.87% improvement in prediction accuracy compared with the traditional BPNN model. The correlation coefficient R2 for predicted crop nitrogen and water content was 0.9045 and 0.8734, respectively. The experimental results demonstrate high training efficiency and accuracy. This method lays a strong foundation for developing precision irrigation and fertilization plans for modern agriculture and holds promising prospects.

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Section: Construction Of Prediction Model Of Crop Water and Nitrogen ...mentioning

confidence: 99%

Enhanced Neural Network for Rapid Identification of Crop Water and Nitrogen Content Using Multispectral Imaging

Peng,

He,

Zheng

et al. 2023

Agronomy

View full text Add to dashboard Cite

show abstract

“…Metaheuristic or intelligent algorithms are being mostly used in engineering fields with domains including power system [8], [9], electrical drives [5], [7] industrial engineering [10], and mechanical engineering [11], [12]. However, literature review reveals that various optimization algorithms do exist to optimize any controller for solving any real-world application.…”

Section: Introductionmentioning

confidence: 99%

An intelligent PID controller tuning for speed control of BLDC motor using driving training-based optimization

Sarma,

Bardalai

2023

IJPEDS

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Tuning of proportional-integral-derivative (PID) controller remains a matter of great concern for the control engineers as it plays a major role to obtain optimal performance of any system Due to their simplicity and excellent efficiency, metaheuristic algorithms have recently become extremely popular among researchers for handling a wide range of real-world optimization challenges. In order to optimize a PID controller for managing the speed of a BLDC motor, this work proposes a novel application of the driving training-based optimization (DTBO) algorithm, one of the latest and most recent human-based metaheuristic algorithms. The purpose of this present study is to optimize a PID controller for a BLDC motor speed control by DTBO method and evaluate its performance with a similar controller tuned by grey wolf optimization (GWO) method. Additionally, the suggested DTBO-PID controller's robustness analysis is being carried out with BLDC motor parameter modifications as well as a comparison to the GWO-PID controller. The comparison is carried out in MATLAB/Simulink, and the results are based on common step response metrics such rise time, settling time, and maximum overshoot. For easier comprehension, the results are presented in tabular and graphical form. The chosen BLDC motor drive system's selected DTBO-PID controller performs better and is more reliable than the GWO-PID controller, according to the final simulation findings.

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PSO with improved local unimodal search ability for incipient pump fault identification

Singh,

Tripathi,

Khanna

2023

Sādhanā

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Chaotic inertia weight and constriction factor-based PSO algorithm for BLDC motor drive control

Cited by 5 publications

References 0 publications

Enhanced Neural Network for Rapid Identification of Crop Water and Nitrogen Content Using Multispectral Imaging

Enhanced Neural Network for Rapid Identification of Crop Water and Nitrogen Content Using Multispectral Imaging

An intelligent PID controller tuning for speed control of BLDC motor using driving training-based optimization

PSO with improved local unimodal search ability for incipient pump fault identification

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