Novel Explicit Model Predictive Control Strategy For Boost Converters Based on State-space Averaging Method

IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society Pub Date : 2022-10-17 DOI:10.1109/IECON49645.2022.9968518

Zhaohong Wang, Ke Xu, Yonghong Lan, Xiaofan Yang

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Abstract

A novel explicit model predictive control strategy is proposed for DC-DC converters in this study. Firstly, the state-space models of boost converter are established, both on-state and off-state respectively. By characteristic analysis of state-space functions, the control target is reconfigured as a linear parametric-varying (LPV) model with time-variant state matrices. Towards such target, then an explicit model predictive controller (MPC) is proposed in order to enhance transition dynamics. A novel prediction model is designed by utilizing of Tylor series. Moreover, estimated average states are given as one of the objective variables in cost function by measurement of state-space averaging (SSA) method. Consequently, the computational load of boost converter control system is alleviated adequately. At the end, two numerical simulations of voltage tracking are performed, one in waveform of slope and the other is sinusoidal. The results show remarkable performances of rapid response without any steady-state errors.

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基于状态空间平均法的新型升压变换器显式模型预测控制策略

本文提出了一种新的DC-DC变换器显式模型预测控制策略。首先，建立了升压变换器的状态空间模型，分别建立了导通和关断状态模型。通过状态空间函数的特征分析，将控制目标重构为具有时变状态矩阵的线性参数变(LPV)模型。针对这一目标，提出了一种显式模型预测控制器(MPC)来增强过渡动力学。利用泰勒序列设计了一种新的预测模型。此外，通过测量状态空间平均(SSA)方法，给出了估计的平均状态作为代价函数的目标变量之一。从而充分减轻了升压变换器控制系统的计算负荷。最后，进行了两种电压跟踪的数值模拟，一种是斜率波形，另一种是正弦波形。结果表明，该方法具有良好的快速响应性能，无稳态误差。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society

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