HGO and neural network based integral sliding mode control for PMSMs with uncertainty
This paper proposes an integral sliding mode control that integrates a high-gain observer (HGO) and a radial basis function neural network (RBFNN) for a permanent magnet synchronous motor (PMSM) with uncertainty. Since the second-order motion equation of the PMSM is used to improve the control perfo...
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| Vydáno v: | JOURNAL OF POWER ELECTRONICS Ročník 20; číslo 5; s. 1206 - 1221 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Singapore
Springer Singapore
01.09.2020
Springer Nature B.V 전력전자학회 |
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| ISSN: | 1598-2092, 2093-4718 |
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| Abstract | This paper proposes an integral sliding mode control that integrates a high-gain observer (HGO) and a radial basis function neural network (RBFNN) for a permanent magnet synchronous motor (PMSM) with uncertainty. Since the second-order motion equation of the PMSM is used to improve the control performance, the speed derivative, which cannot be measured directly, is required. Thus, the HGO is designed to estimate the unknown state (speed derivative). In addition, the RBFNN is designed to approximate the compounded disturbance including the lumped disturbance of system and the HGO error effect. Unlike previous studies, the output of the RBFNN is compensated by both the controller and the HGO to improve the system robustness and observer accuracy. The sliding function and the HGO error are both taken into account in the RBFNN to explicitly guarantee the stability of the whole system. To demonstrate the superiority of the proposed method, comparative simulations and experiments were carried out in different cases. |
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| AbstractList | This paper proposes an integral sliding mode control that integrates a high-gain observer (HGO) and a radial basis function neural network (RBFNN) for a permanent magnet synchronous motor (PMSM) with uncertainty. Since the second-order motion equation of the PMSM is used to improve the control performance, the speed derivative, which cannot be measured directly, is required. Thus, the HGO is designed to estimate the unknown state (speed derivative). In addition, the RBFNN is designed to approximate the compounded disturbance including the lumped disturbance of system and the HGO error effect. Unlike previous studies, the output of the RBFNN is compensated by both the controller and the HGO to improve the system robustness and observer accuracy. The sliding function and the HGO error are both taken into account in the RBFNN to explicitly guarantee the stability of the whole system. To demonstrate the superiority of the proposed method, comparative simulations and experiments were carried out in different cases. KCI Citation Count: 0 This paper proposes an integral sliding mode control that integrates a high-gain observer (HGO) and a radial basis function neural network (RBFNN) for a permanent magnet synchronous motor (PMSM) with uncertainty. Since the second-order motion equation of the PMSM is used to improve the control performance, the speed derivative, which cannot be measured directly, is required. Thus, the HGO is designed to estimate the unknown state (speed derivative). In addition, the RBFNN is designed to approximate the compounded disturbance including the lumped disturbance of system and the HGO error effect. Unlike previous studies, the output of the RBFNN is compensated by both the controller and the HGO to improve the system robustness and observer accuracy. The sliding function and the HGO error are both taken into account in the RBFNN to explicitly guarantee the stability of the whole system. To demonstrate the superiority of the proposed method, comparative simulations and experiments were carried out in different cases. |
| Author | Ma, Xikui Ge, Yang Yang, Lihui |
| Author_xml | – sequence: 1 givenname: Yang surname: Ge fullname: Ge, Yang organization: The State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University – sequence: 2 givenname: Lihui orcidid: 0000-0001-7594-4653 surname: Yang fullname: Yang, Lihui email: lihui.yang@mail.xjtu.edu.cn organization: The State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University – sequence: 3 givenname: Xikui surname: Ma fullname: Ma, Xikui organization: The State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University |
| BackLink | https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART002625666$$DAccess content in National Research Foundation of Korea (NRF) |
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| Keywords | Integral sliding mode control Parameter adaptive algorithm Permanent magnet synchronous motor Radial basis function neural network High-gain observer |
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| Snippet | This paper proposes an integral sliding mode control that integrates a high-gain observer (HGO) and a radial basis function neural network (RBFNN) for a... |
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| SubjectTerms | Accuracy Algorithms Controllers Design Electrical Machines and Networks Engineering Equations of motion High gain Neural networks Original Article Permanent magnets Power Electronics Radial basis function Sliding mode control Synchronous motors Systems stability Uncertainty 전기공학 |
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| Title | HGO and neural network based integral sliding mode control for PMSMs with uncertainty |
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