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165 results on '"Ma, Jien"'

11. Model-Free Moving-Discretized-Control-Set Predictive Control for Three-Phase Dual-Active-Bridge Converters

Author

Sun, Jiahao, Qiu, Lin, Liu, Xing, Ma, Jien, Rodriguez, Jose, and Fang, Youtong

Abstract

Moving-discretized-control-set model predictive control is a powerful control scheme for dual-active-bridge (DAB) converter topologies, given its outstanding features in terms of fast dynamics and multiobjective optimization. However, the inaccuracy of the system model will degrade its performance, and the high-precision model of the DAB will also aggravate the complexity of controller design. To circumvent this issue, a model-free moving-discretized-control-set predictive control (MF-MDCSPC) scheme is first proposed for three-phase DAB (3p-DAB) converters in this article. First, a novel data-driven model is proposed to replace the traditional mathematical model of the 3p-DAB, which is built with the control input and output variables of the 3p-DAB. Next, an adaptive forgetting factor recursive least square algorithm is developed to estimate the parameters of the data-driven model, and a novel MF-MDCSPC based on the data-driven model is designed to enhance the parameter robustness of the 3p-DAB. Furthermore, a multilayer recursive method is presented to estimate the parameters of the proposed data-driven model in the predictive horizon, and an improved adaptive step mechanism is developed to realize a fast transition. Finally, comprehensive simulation and experimental results from a down-scale laboratory prototype are provided to verify the theoretic analysis and the efficiency of the proposed methodology.

Published
2024
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20. Dynamic-Linearization-Based Predictive Control of a Voltage-Source Inverter

Author

Wu, Wenjie, Qiu, Lin, Liu, Xing, Ma, Jien, Rodriguez, Jose, and Fang, Youtong

Abstract

In pursuit of accurate and fast trajectory tracking of power converters, an explicit model is commonly used in the finite control-set model predictive control (FCS-MPC) framework to predict precise behaviors of controlled variables. In reality, however, the model mismatch is inevitable, which causes the inherent challenges of parameter sensitivity and model uncertainties of the FCS-MPC method. This article proposes a dynamic-linearization-based predictive control architecture to circumvent such model dependence while keeping the attractive features of the conventional FCS-MPC method. By integrating the data-driven feature of the dynamic-linearization approach, the detailed model used in the FCS-MPC controller is replaced by a virtual equivalent data model, creating a data-driven predictive control architecture. The suggested method selects optimal control action solely based on the input–output data, exhibiting strong rejection against parameter variations while inheriting the distinctive property of the conventional FCS-MPC method. Finally, the proposed design is validated through comparative simulation and experimental results on a three-level neutral-point-clamped inverter.

Published
2024
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