基于单电阻采样的永磁同步电机有限集模型预测控制

doi:10.19595/j.cnki.1000-6753.tces.242388

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Abstract Finite-control-set model predictive control (FCS-MPC) has garnered significant attention in permanent magnet synchronous motor (PMSM) control due to its simple structure, multi-objective coordination capability, and nonlinear optimization. Like other high-performance control strategies for PMSMs, FCS-MPC requires real-time phase current sampling. Since current sensors increase both cost and system size, low-power drive systems commonly utilize single-resistor DC-link current sampling. However, during phase current reconstruction, the presence of a dead zone distorts current acquisition, thereby compromising control accuracy. Existing dead-time compensation methods are generally designed for modulated systems and cannot be directly applied to modulator-free discrete switching strategies such as FCS-MPC. Therefore, this paper proposes a finite-control-set model predictive control algorithm based on single-resistor current sampling.
Firstly, under zero-vector states of the three-phase inverter, no phase current information is available through single-resistor sampling. In such cases, the current is estimated using the predictive model. During active vector states, the current of one phase is measured via the DC-link resistor, while another phase's current is predicted using the FCS-MPC model. The three-phase currents are then reconstructed based on their balanced relationship. Secondly, to improve reconstruction accuracy, a lumped-parameter model is developed along with a gradient-descent-based adaptive parameter identification algorithm, enabling online parameter estimation and reducing reliance on accurate model parameters. Thirdly, a continuous-state penalty mechanism is introduced by adding a penalty term to the cost function, thereby avoiding model update stagnation.
Finally, compared with a conventional SVPWM-based single-resistor sampling method, the proposed current-sampling algorithm was tested on a permanent-magnet synchronous motor driving an oil pump. Two Hall sensors measured the phase currents as a reference for accuracy evaluation. Experimental results show that the proposed method accurately reconstructs the three-phase currents under identical operating conditions, keeping errors within a small margin. The root-mean-square error between reconstructed and sensed currents is reduced by approximately 50% compared to the conventional approach. The proposed algorithm also achieved better total harmonic distortion (THD) performance across different speeds. Starting, reversal, and sudden-load tests verified its dynamic performance and support online parameter identification. The continuous state penalty mechanism has been validated during normal motor operation.
The following conclusions can be drawn. (1) A finite control set model predictive control algorithm based on single-resistor sampling is proposed. The issue of FCS-MPC unsuitability for single-resistor sampling scenarios is addressed by employing current reconstruction with the predictive model. (2) Online identification using a lumped parameter model reduces the dependency on precise motor parameters during current reconstruction, thereby improving the accuracy of the reconstructed currents. (3) A continuous state penalty mechanism is proposed to resolve model update stagnation caused by underexcitation during specific operating conditions.

Key words： Finite-control-set model predictive control (FCS-MPC) single-resistor sampling current sampling continuous state penalty mechanism

Received: 30 December 2024

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	Zhao Yue
	Chen Zhuoyi
	Shen Feifan
	Liu Qianyu

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Zhao Yue,Chen Zhuoyi,Shen Feifan等. Finite-Control-Set Model Predictive Control of Permanent Magnet Synchronous Motor Based on Single Resistor Sampling[J]. Transactions of China Electrotechnical Society, 2026, 41(2): 487-498.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.242388 OR https://dgjsxb.ces-transaction.com/EN/Y2026/V41/I2/487

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