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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Veröffentlicht in:IEEE transactions on industrial electronics (1982) Jg. 72; H. 3; S. 2244 - 2253
Hauptverfasser: Wu, Wenjie, Qiu, Lin, Liu, Xing, Ma, Jien, Rodriguez, Jose, Fang, Youtong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.03.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
ISSN:0278-0046, 1557-9948
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Zusammenfassung: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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content type line 14
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2024.3413837