An effective speed regulation of brushless DC motor using hybrid approach. (December 2022)
- Record Type:
- Journal Article
- Title:
- An effective speed regulation of brushless DC motor using hybrid approach. (December 2022)
- Main Title:
- An effective speed regulation of brushless DC motor using hybrid approach
- Authors:
- Arivalahan, R.
Venkatesh, S.
Vinoth, T. - Abstract:
- Highlights: Proposed a hybrid method for speed of the BLDC motor. The RBFNN is trained and processed first, then that output is given to the SPOA approach. The proposed RBFNN-SPOA approach is tune the parameter of PID controller. Abstract: In this paper, a hybrid method is proposed to achieve an effective control of the speed of the BLDC motor with the established specifications. The proposed hybrid system is the joined execution of Radial Basis Function Neural Network (RBFNN) and Student psychology optimization algorithm (SPOA) and hence it is named as RBFNN-SPOA strategy. The RBFNN is trained and processed first, then that output is given to the SPOA approach, which drives the BLDC motor. The proposed RBFNN-SPOA approach is tune the parameter of PID controller, through which the speed regulation process of motor is achieved. Based on the load variation and the variation of input, the proposed approach is analyzed. The best gain parameter of the PID controller is utilized to control the speed of the motor. The proposed approach is considering the constraints, which are utilized to obtain the objective of the system. The factors like rise time, peak time, peak overshoot, settling time and steady-state error is assessed using the proposed approach. By then, the performance of the proposed method is executed on MATLAB platform and compared with existing methods. The proposed method Peak overshoot, Peak undershoot value becomes 9.67 rpm and 2.05 rpm. The settling time ofHighlights: Proposed a hybrid method for speed of the BLDC motor. The RBFNN is trained and processed first, then that output is given to the SPOA approach. The proposed RBFNN-SPOA approach is tune the parameter of PID controller. Abstract: In this paper, a hybrid method is proposed to achieve an effective control of the speed of the BLDC motor with the established specifications. The proposed hybrid system is the joined execution of Radial Basis Function Neural Network (RBFNN) and Student psychology optimization algorithm (SPOA) and hence it is named as RBFNN-SPOA strategy. The RBFNN is trained and processed first, then that output is given to the SPOA approach, which drives the BLDC motor. The proposed RBFNN-SPOA approach is tune the parameter of PID controller, through which the speed regulation process of motor is achieved. Based on the load variation and the variation of input, the proposed approach is analyzed. The best gain parameter of the PID controller is utilized to control the speed of the motor. The proposed approach is considering the constraints, which are utilized to obtain the objective of the system. The factors like rise time, peak time, peak overshoot, settling time and steady-state error is assessed using the proposed approach. By then, the performance of the proposed method is executed on MATLAB platform and compared with existing methods. The proposed method Peak overshoot, Peak undershoot value becomes 9.67 rpm and 2.05 rpm. The settling time of proposed approach is 0.009 s. The steady state error at speed and steady state error percentage of proposed approach is 0.25 rpm, 0.008 rpm; it is less than the existing approach under no load condition. … (more)
- Is Part Of:
- Advances in engineering software. Volume 174(2022)
- Journal:
- Advances in engineering software
- Issue:
- Volume 174(2022)
- Issue Display:
- Volume 174, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 174
- Issue:
- 2022
- Issue Sort Value:
- 2022-0174-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-12
- Subjects:
- Peak overshoot -- Peak undershoot -- Speed control -- Steady state error -- Gain parameter -- Reduction of error -- Settling time -- Overshoot -- Rise time -- Peak time
Computer-aided engineering -- Periodicals
Engineering -- Computer programs -- Periodicals
Engineering -- Software -- Periodicals
Periodicals
620.0028553 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09659978 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.advengsoft.2022.103321 ↗
- Languages:
- English
- ISSNs:
- 0965-9978
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0705.450000
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