Hierarchical search approach based online model predictive control of three‐level converter fed PMSM driving systems. Issue 15 (4th June 2022)

• Record Type: Journal Article
• Authors: Guan, Bo; Zhen, Xiaoliang; Yan, Xuyang; Du, Xiong
• Is Part Of: IET power electronics. Volume 15:Issue 15(2022)
• Page Start: 1617
• Page End: 1628
• Publication Date: 2022-06-04
• Subjects: Power electronics -- Periodicals; 621.31705
• DOI: 10.1049/pel2.12331
• Languages: English
• ISSNs: 1755-4535

Abstract

Abstract: The finite‐control‐set model predictive control (FCS‐MPC) can achieve a high output and fast control response. However, the size of the candidate vector set of FCS‐MPC must be very huge to achieve a satisfactory steady‐state control performance. Although some studies suggest that the candidate vector set can be expanded by increasing the number of vectors in one period or directly defining new virtual vectors, there is a tricky contradiction between improving the steady‐state control performance and reducing the computational burden. It makes the online realization difficult. This study presents a hierarchical search approach based FCS‐MPC of three‐level converter fed permanent magnet synchronous motor (PMSM) driving systems. The optimal vector is obtained by continuously shrinking the vector space, so that only 30 prediction calculations are needed to evaluate more than 20, 000 candidate vectors. Consequently, the proposed method can achieve the excellent steady‐and transient‐state control performance online. Moreover, since the optimal vector is chosen directly from the vector set, the linear‐and over‐modulation are unified. No special treatment is necessary for over‐modulation driving systems, because the reference voltage is decided naturally by the cost function of FCS‐MPC. Some experimental results are shown finally to prove the correctness and feasibility of the proposed method.