Lion swarm optimisation‐based tuning method for generalised predictive fractional‐order PI to control the speed of brushless direct current motor
In order to enhance the speed tracking performance of brushless direct current motor (BLDCM), a new generalised predictive fractional‐order PI (GPFOPI) control algorithm ground on lion swarm optimisation (LSO‐GPFOPI) is proposed in this paper. First, GPFOPI combines robust generalised predictive con...
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| Veröffentlicht in: | IET electric power applications Jg. 16; H. 8; S. 879 - 895 |
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| Sprache: | Englisch |
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01.08.2022
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| Abstract | In order to enhance the speed tracking performance of brushless direct current motor (BLDCM), a new generalised predictive fractional‐order PI (GPFOPI) control algorithm ground on lion swarm optimisation (LSO‐GPFOPI) is proposed in this paper. First, GPFOPI combines robust generalised predictive control with scalable fractional‐order PI, whose optimal control law is obtained through the model parameters and the predicted speed reference values. Next, the lion swarm algorithm is utilised to optimally tune the three parameters KP,KI,λ ${K}_{P},{K}_{I},\lambda $ of GPFOPI, so as to make them be optimised. Finally, the optimal control signal is input into the system of BLDCM to control its speed. To verify the effectiveness of LSO‐GPFOPI, its performance is compared with other algorithms. The results show that LSO‐GPFOPI has better response speed, speed tracking ability, anti‐interference ability, robustness, and self‐adaptability than other algorithms. Moreover, an experimental platform of the BLDCM drive system is built to verify the feasibility of the LSO‐GPOPI algorithm, and the test results prove that LSO‐GPOPI can guarantee the smooth and accurate operation of BLDCM. |
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| AbstractList | In order to enhance the speed tracking performance of brushless direct current motor (BLDCM), a new generalised predictive fractional‐order PI (GPFOPI) control algorithm ground on lion swarm optimisation (LSO‐GPFOPI) is proposed in this paper. First, GPFOPI combines robust generalised predictive control with scalable fractional‐order PI, whose optimal control law is obtained through the model parameters and the predicted speed reference values. Next, the lion swarm algorithm is utilised to optimally tune the three parameters KP,KI,λ ${K}_{P},{K}_{I},\lambda $ of GPFOPI, so as to make them be optimised. Finally, the optimal control signal is input into the system of BLDCM to control its speed. To verify the effectiveness of LSO‐GPFOPI, its performance is compared with other algorithms. The results show that LSO‐GPFOPI has better response speed, speed tracking ability, anti‐interference ability, robustness, and self‐adaptability than other algorithms. Moreover, an experimental platform of the BLDCM drive system is built to verify the feasibility of the LSO‐GPOPI algorithm, and the test results prove that LSO‐GPOPI can guarantee the smooth and accurate operation of BLDCM. In order to enhance the speed tracking performance of brushless direct current motor (BLDCM), a new generalised predictive fractional‐order PI (GPFOPI) control algorithm ground on lion swarm optimisation (LSO‐GPFOPI) is proposed in this paper. First, GPFOPI combines robust generalised predictive control with scalable fractional‐order PI, whose optimal control law is obtained through the model parameters and the predicted speed reference values. Next, the lion swarm algorithm is utilised to optimally tune the three parameters of GPFOPI, so as to make them be optimised. Finally, the optimal control signal is input into the system of BLDCM to control its speed. To verify the effectiveness of LSO‐GPFOPI, its performance is compared with other algorithms. The results show that LSO‐GPFOPI has better response speed, speed tracking ability, anti‐interference ability, robustness, and self‐adaptability than other algorithms. Moreover, an experimental platform of the BLDCM drive system is built to verify the feasibility of the LSO‐GPOPI algorithm, and the test results prove that LSO‐GPOPI can guarantee the smooth and accurate operation of BLDCM. |
| Author | Wang, Tingting Wang, Chuhang Qing, Jinhui Wang, Hongzhi Hu, Huangshui |
| Author_xml | – sequence: 1 givenname: Tingting surname: Wang fullname: Wang, Tingting organization: Changchun University of Technology – sequence: 2 givenname: Hongzhi orcidid: 0000-0002-0429-6932 surname: Wang fullname: Wang, Hongzhi email: wanghongzhi@ccut.edu.cn organization: Changchun University of Technology – sequence: 3 givenname: Huangshui surname: Hu fullname: Hu, Huangshui organization: Changchun University of Technology – sequence: 4 givenname: Jinhui surname: Qing fullname: Qing, Jinhui organization: Changchun University of Technology – sequence: 5 givenname: Chuhang surname: Wang fullname: Wang, Chuhang organization: Changchun Normal University |
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| Cites_doi | 10.1109/tmag.2019.2950953 10.1142/s0219691319410042 10.1080/00051144.2019.1596014 10.1109/tte.2020.3010279 10.1155/2019/2671792 10.1109/tie.2012.2201432 10.4025/actascitechnol.v39i3.28416 10.1177/0142331219887844 10.1016/j.jfranklin.2018.07.004 10.1002/asjc.664 10.1108/aeat‐01‐2018‐0041 10.1109/APCET.2012.6302000 10.1016/j.micpro.2019.102893 10.1007/s00542‐019‐04411‐5 10.1016/j.jestch.2018.05.003 10.1109/access.2019.2902888 10.1016/j.jclepro.2019.118612 10.1142/s0218126618501232 10.3906/elk‐1612‐277 10.6113/jpe.2016.16.1.205 10.1016/j.ifacol.2017.08.1416 10.1177/168781402096898 10.1016/j.isatra.2018.06.012 10.1007/s00521‐019‐04090‐3 10.1109/access.2020.2980390 10.1007/s42835‐021‐00660‐5 10.1016/b978‐0‐12‐816152‐4.00018‐2 10.1016/j.asej.2018.07.005 10.1142/s0219843619500427 10.1109/access.2020.2966437 10.1007/s00500‐019‐04532‐z 10.1049/iet‐epa.2017.0615 10.1109/tie.2016.2521338 10.23919/ICEMS.2018.8549407 10.1016/j.isatra.2012.04.006 10.1177/0142331217751040 10.1109/CIMSA.2011.6059932 |
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| Title | Lion swarm optimisation‐based tuning method for generalised predictive fractional‐order PI to control the speed of brushless direct current motor |
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