FCS-MPC Based on Dimension Unification Cost Function

Jinyang Han, Hao Yuan, Weichao Li (), Liang Zhou, Chen Deng and Ming Yan
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Jinyang Han: National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China
Hao Yuan: School of Electrical Engineering, Southeast University, Nanjing 210018, China
Weichao Li: National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China
Liang Zhou: National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China
Chen Deng: National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China
Ming Yan: National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China

Energies, 2024, vol. 17, issue 11, 1-15

Abstract: Finite Control Set Model Predictive Control (FCS-MPC) has the ability to achieve multi-objective optimization, but there are still many challenges. The key to realizing multi-objective optimization in FCS-MPC lies in the design of the cost function. However, the different dimensions of penalty terms in the cost function often lead to difficulties in designing weighting coefficients. Incorrect weighting coefficients may result in truncation errors in calculations of DSPs and FPGAs, thereby affecting the algorithm’s control performance. Therefore, this article focuses on a system driving an induction motor with a three-level Neutral Point Clamped (NPC) inverter, and selects stator current and switching frequency as penalty terms in the cost function. An improved method is proposed to unify the dimensions of both penalty terms in the cost function. By unifying the dimensions of the penalty terms, a simple design of weighting coefficients can be achieved. Subsequently, to balance the inverter’s switching frequency and the dynamic response performance of the motor, a composite cost function is further proposed. Finally, the rationality of the proposed method is validated through simulation and experimental platforms.

Keywords: finite control set model predictive control; FCS-MPC; cost function design; dimensionally unified (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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