Hunting Based Optimization Techniques Used in Controlling an Active Magnetic Bearing System
This paper proposes a closed-loop and implements some metaheuristic optimization approaches to regulate an unstable active magnetic bearing (AMB) system. First of all, a hardware model of an AMB is fabricated in the laboratory. Mathematical analysis is carried out and a linearized open-loop transfer...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9795008/ |
| _version_ | 1828831913874292736 |
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| author | Suraj Gupta Pabitra Kumar Biswas Thanikanti Sudhakar Babu Hassan Haes Alhelou |
| author_facet | Suraj Gupta Pabitra Kumar Biswas Thanikanti Sudhakar Babu Hassan Haes Alhelou |
| author_sort | Suraj Gupta |
| collection | DOAJ |
| description | This paper proposes a closed-loop and implements some metaheuristic optimization approaches to regulate an unstable active magnetic bearing (AMB) system. First of all, a hardware model of an AMB is fabricated in the laboratory. Mathematical analysis is carried out and a linearized open-loop transfer function is obtained for an equilibrium point of operation, using the parameters of the hardware model. Then, a closed loop is proposed for this AMB system, which comprises a PID controller, power amplifier, and position sensor. Further, three different metaheuristic nature-inspired hunting-based optimization algorithms i.e., Ant lion optimization (ALO), Grey wolf optimization (GWO), and Whale optimization algorithm (WOA) are implemented individually to calculate the gain parameters of the PID controller. Separately, the performance of these optimization algorithms is evaluated and observed on four different performance indices: integral of absolute error (IAE), integral of squared error (ISE), and an integral of time multiplied absolute error (ITAE) and integral of time multiplied squared error (ITSE). For a stable, efficient, and reliable bearing operation, it is vital to perform an analysis of the performance of optimization techniques with different objective functions for the proposed system. Therefore, few comparisons are conducted, first based on data obtained from statistical analysis. The second is based on data obtained from transient state performance and phase margin. Third on the scale of algorithm execution time. Finally, with the assistance of observed data effectiveness of each optimization technique to control the proposed AMB system is concluded which can serve as theoretical and experimental foundations for the continued use of AMB in high-speed applications. |
| first_indexed | 2024-12-12T16:42:56Z |
| format | Article |
| id | doaj.art-3b04ab3adfa94d55901791382f2fdf46 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T16:42:56Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3b04ab3adfa94d55901791382f2fdf462022-12-22T00:18:32ZengIEEEIEEE Access2169-35362022-01-0110627026272110.1109/ACCESS.2022.31828759795008Hunting Based Optimization Techniques Used in Controlling an Active Magnetic Bearing SystemSuraj Gupta0Pabitra Kumar Biswas1Thanikanti Sudhakar Babu2https://orcid.org/0000-0003-0737-3961Hassan Haes Alhelou3https://orcid.org/0000-0002-7427-2848National Institute of Technology Mizoram, Aizawl, IndiaNational Institute of Technology Mizoram, Aizawl, IndiaDepartment of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad, IndiaDepartment of Electrical Power Engineering, Faculty of Mechanical and Electrical Engineering, Tishreen University, Latakia, SyriaThis paper proposes a closed-loop and implements some metaheuristic optimization approaches to regulate an unstable active magnetic bearing (AMB) system. First of all, a hardware model of an AMB is fabricated in the laboratory. Mathematical analysis is carried out and a linearized open-loop transfer function is obtained for an equilibrium point of operation, using the parameters of the hardware model. Then, a closed loop is proposed for this AMB system, which comprises a PID controller, power amplifier, and position sensor. Further, three different metaheuristic nature-inspired hunting-based optimization algorithms i.e., Ant lion optimization (ALO), Grey wolf optimization (GWO), and Whale optimization algorithm (WOA) are implemented individually to calculate the gain parameters of the PID controller. Separately, the performance of these optimization algorithms is evaluated and observed on four different performance indices: integral of absolute error (IAE), integral of squared error (ISE), and an integral of time multiplied absolute error (ITAE) and integral of time multiplied squared error (ITSE). For a stable, efficient, and reliable bearing operation, it is vital to perform an analysis of the performance of optimization techniques with different objective functions for the proposed system. Therefore, few comparisons are conducted, first based on data obtained from statistical analysis. The second is based on data obtained from transient state performance and phase margin. Third on the scale of algorithm execution time. Finally, with the assistance of observed data effectiveness of each optimization technique to control the proposed AMB system is concluded which can serve as theoretical and experimental foundations for the continued use of AMB in high-speed applications.https://ieeexplore.ieee.org/document/9795008/Active magnetic bearingmetaheuristicnature-inspired hunting-based algorithmsAnt lion optimizationGrey wolf optimizationWhale optimization algorithm |
| spellingShingle | Suraj Gupta Pabitra Kumar Biswas Thanikanti Sudhakar Babu Hassan Haes Alhelou Hunting Based Optimization Techniques Used in Controlling an Active Magnetic Bearing System IEEE Access Active magnetic bearing metaheuristic nature-inspired hunting-based algorithms Ant lion optimization Grey wolf optimization Whale optimization algorithm |
| title | Hunting Based Optimization Techniques Used in Controlling an Active Magnetic Bearing System |
| title_full | Hunting Based Optimization Techniques Used in Controlling an Active Magnetic Bearing System |
| title_fullStr | Hunting Based Optimization Techniques Used in Controlling an Active Magnetic Bearing System |
| title_full_unstemmed | Hunting Based Optimization Techniques Used in Controlling an Active Magnetic Bearing System |
| title_short | Hunting Based Optimization Techniques Used in Controlling an Active Magnetic Bearing System |
| title_sort | hunting based optimization techniques used in controlling an active magnetic bearing system |
| topic | Active magnetic bearing metaheuristic nature-inspired hunting-based algorithms Ant lion optimization Grey wolf optimization Whale optimization algorithm |
| url | https://ieeexplore.ieee.org/document/9795008/ |
| work_keys_str_mv | AT surajgupta huntingbasedoptimizationtechniquesusedincontrollinganactivemagneticbearingsystem AT pabitrakumarbiswas huntingbasedoptimizationtechniquesusedincontrollinganactivemagneticbearingsystem AT thanikantisudhakarbabu huntingbasedoptimizationtechniquesusedincontrollinganactivemagneticbearingsystem AT hassanhaesalhelou huntingbasedoptimizationtechniquesusedincontrollinganactivemagneticbearingsystem |