Data-Enabled Finite State Predictive Control for Power Converters via Adaline Neural Network
Finite control-set model predictive control (FCS-MPC) has been found as a promising alternative in the control of power converters and motor drives, albeit with model dependence issues. This inherent defect of the FCS-MPC controller triggered the widespread of model-free or data-driven control schem...
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Published in | IEEE transactions on industrial electronics (1982) Vol. 72; no. 3; pp. 2244 - 2253 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
New York
IEEE
01.03.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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Abstract | Finite control-set model predictive control (FCS-MPC) has been found as a promising alternative in the control of power converters and motor drives, albeit with model dependence issues. This inherent defect of the FCS-MPC controller triggered the widespread of model-free or data-driven control schemes in recent decades. This article, at hand, presents a data-enabled finite set predictive control solution subject to model dependence issues from the dynamic modeling point of view. In this regard, a dynamic-linearization data model is utilized to equivalently reformulate the governed power converter at each operation point. In pursuit of the accurate modeling of the plant, the time-varying parameters of the data model are updated online by an adaptive linear neural network, rendering a favorable influence on implementation. Additionally, an improved capacitance-less voltage balancing method is proposed to regulate the neutral point potential. Since the parameterless prediction process for both load currents and capacitor voltage relies solely on measured and historical input-output data of the plant, the destructive effect of parameter variations can be circumvented. To evaluate the correctness of the proposed solution, the comparative simulation and experimentation with the conventional method and state-of-the-art solutions are examined on a classic three-level neutral-point-clamped inverter. |
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AbstractList | Finite control-set model predictive control (FCS-MPC) has been found as a promising alternative in the control of power converters and motor drives, albeit with model dependence issues. This inherent defect of the FCS-MPC controller triggered the widespread of model-free or data-driven control schemes in recent decades. This article, at hand, presents a data-enabled finite set predictive control solution subject to model dependence issues from the dynamic modeling point of view. In this regard, a dynamic-linearization data model is utilized to equivalently reformulate the governed power converter at each operation point. In pursuit of the accurate modeling of the plant, the time-varying parameters of the data model are updated online by an adaptive linear neural network, rendering a favorable influence on implementation. Additionally, an improved capacitance-less voltage balancing method is proposed to regulate the neutral point potential. Since the parameterless prediction process for both load currents and capacitor voltage relies solely on measured and historical input–output data of the plant, the destructive effect of parameter variations can be circumvented. To evaluate the correctness of the proposed solution, the comparative simulation and experimentation with the conventional method and state-of-the-art solutions are examined on a classic three-level neutral-point-clamped inverter. |
Author | Qiu, Lin Liu, Xing Ma, Jien Fang, Youtong Rodriguez, Jose Wu, Wenjie |
Author_xml | – sequence: 1 givenname: Wenjie orcidid: 0000-0001-9173-7099 surname: Wu fullname: Wu, Wenjie email: wuwenjie@zju.edu.cn organization: College of Electrical Engineering, Zhejiang University, Hangzhou, China – sequence: 2 givenname: Lin orcidid: 0000-0003-1236-2191 surname: Qiu fullname: Qiu, Lin email: qiu_lin@zju.edu.cn organization: College of Electrical Engineering, Zhejiang University, Hangzhou, China – sequence: 3 givenname: Xing orcidid: 0000-0001-9685-2862 surname: Liu fullname: Liu, Xing email: xingldl@zju.edu.cn organization: College of Electrical Engineering, Shanghai Dianji University, Shanghai, China – sequence: 4 givenname: Jien orcidid: 0000-0001-6970-3634 surname: Ma fullname: Ma, Jien email: majien@zju.edu.cn organization: College of Electrical Engineering, Zhejiang University, Hangzhou, China – sequence: 5 givenname: Jose orcidid: 0000-0002-1410-4121 surname: Rodriguez fullname: Rodriguez, Jose email: jose.rodriguezp@uss.cl organization: Faculty of Engineering, Universidad San Sebastian Santiago, Santiago, Chile – sequence: 6 givenname: Youtong orcidid: 0000-0002-8521-4184 surname: Fang fullname: Fang, Youtong email: youtong@zju.edu.cn organization: College of Electrical Engineering, Zhejiang University, Hangzhou, China |
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Snippet | Finite control-set model predictive control (FCS-MPC) has been found as a promising alternative in the control of power converters and motor drives, albeit... |
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SubjectTerms | Adaline Adaptation models Capacitors Data models Dynamic models dynamic-linearization Electric potential Inverters model predictive control (MPC) Neural networks Parameters Power converters Predictive control Predictive models robustness Simulation Switches Voltage Voltage control |
Title | Data-Enabled Finite State Predictive Control for Power Converters via Adaline Neural Network |
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