A Quasi-Noncascaded DC-Link Voltage Predictive Control of a PWM Converter

IF 7.2 1区 工程技术 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Industrial Electronics Pub Date : 2025-02-05 DOI:10.1109/TIE.2025.3532730
Tao Wang;Han Wu;Lijian Wu
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Abstract

The main function of pulse-width modulation (PWM) converters in many applications is to control the dc-link voltage with good dynamic performance and strong disturbance rejection capability. Finite-control-set model predictive control (FCS-MPC) is known for its merits in both the above aspects, especially the noncascaded FCS-MPC control structure. However, as to be discussed in this article, the noncascaded FCS-MPC cannot be used to directly control the dc-link voltage of PWM converters, due to the problem of multiequilibrium points caused by the essential nonlinear characteristic of PWM converters. Hence, a quasi-noncascaded dc-link voltage predictive control structure is proposed for the first time, which keeps the outstanding dynamic performance and strong disturbance rejection capability of noncascaded FCS-MPC while avoids overcurrent caused by the unexpected equilibrium points. In addition, the proposed method can inherently handle the parameter mismatch issue, which is an important merit for MPC. Experimental results validate the theoretical analysis and performance of the proposed control strategy.
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PWM变换器的准非级联直流电压预测控制
在许多应用中,脉宽调制(PWM)变换器的主要功能是控制直流电压,具有良好的动态性能和较强的抗干扰能力。有限控制集模型预测控制(FCS-MPC)以其在上述两方面的优点而闻名,特别是非级联的FCS-MPC控制结构。然而,正如本文所讨论的,由于PWM变换器本质的非线性特性所导致的多均衡点问题,非级联的FCS-MPC不能直接用于控制PWM变换器的直流电压。因此,首次提出了一种准非级联直流电路电压预测控制结构,既保持了非级联FCS-MPC优异的动态性能和较强的抗扰能力,又避免了意想不到的平衡点引起的过流。此外,该方法还能有效地处理参数不匹配问题,这是MPC的一个重要优点。实验结果验证了所提控制策略的理论分析和性能。
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来源期刊
IEEE Transactions on Industrial Electronics
IEEE Transactions on Industrial Electronics 工程技术-工程:电子与电气
CiteScore
16.80
自引率
9.10%
发文量
1396
审稿时长
6.3 months
期刊介绍: Journal Name: IEEE Transactions on Industrial Electronics Publication Frequency: Monthly Scope: The scope of IEEE Transactions on Industrial Electronics encompasses the following areas: Applications of electronics, controls, and communications in industrial and manufacturing systems and processes. Power electronics and drive control techniques. System control and signal processing. Fault detection and diagnosis. Power systems. Instrumentation, measurement, and testing. Modeling and simulation. Motion control. Robotics. Sensors and actuators. Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems. Factory automation. Communication and computer networks.
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