A Continuous-Control-Set Type Model-Free Predictive Current Control Based on Time-Series for Permanent Magnet Synchronous Motor Drives
Wei Yao1, Ke Dongliang1, Huang Dongxiao1, Wang Fengxiang1, Kang Jinsong2
1. National and Local Joint Engineering Research Center for Electrical Drives and Power Electronics Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Science Jinjiang 362216 China; 2. Institute of Rail Transit Tongji University Shanghai 201804 China
Abstract The finite-control-set type (FCS-type) predictive control method is not ideal for electric vehicles because of its limited control accuracy and high harmonic content. In contrast, the continuous-control-set type (CCS-type) model-free predictive current control (MF-PCC) is more suitable. The model-free predictive current control (MF-PCC) uses a time-series model as a discrete transfer function, which is more compliant with the motion characteristics of the motor system. However, calculating the inverse of the model in the digital processor makes it challenging to apply in the CCS-type. Therefore, a time-series-based CCS-type MF-PCC strategy is proposed in this paper for a permanent magnet synchronous motor (PMSM) driving system. The plant is accurately expressed, and the control strategy is easily realized in the CCS-type by the online building and updating the time-series model based on the sampled data. Firstly, this approach establishes a time-series model and updates the regressive vector summarizing input and output signals based on sampled data. Secondly, all undetermined coefficients in the model are estimated through the recursive least square (RLS) algorithm. Herein, the current operating state is described as a discrete- time transfer function within the model. Finally, according to predictive reference by the Lagrange algorithm, the regressive vector is updated to predict the output signal and generate the control law. This time-series model is easily realized in the CCS type with good accuracy, addresses the problem of the complex calculation process, and eliminates all time-varying physical parameters and their influences in the a priori model of the plant. Simulation and experimental results on a PMSM driving system show that the proposed method resists disturbances and tracks the reference successfully to suit electric vehicle driving operations. The disturbances mainly include changed parameter mismatches, load torque, and DC voltage. Continuous Fourier analysis and accumulated error comparisons between different control strategies demonstrate that the proposed method achieves a range of total harmonic distortion (THD) between 3.3%~3.9%, with an average value of 3.53%. Compared to conventional strategies, the proposed method has the minimum ascending slope of accumulated error of current. Additionally, to analyze the impact of system noise on driver perception, the proposed method is tested under different speed references and load torques. Finally, experimental results are obtained with different parameter mismatches of typical physical parameters, including stator resistance, stator inductance, and magnet flux linkage, and compared using continuous Fourier analysis. The following conclusions can be drawn. (1) The proposed method represents the current operating state of the motor driving system as a time-series model in the CCS-type. Compared with the conventional MF-PCC strategy using ultra-local, it formulates the system as a group of discrete-time transfer functions. (2) The obtained current quality, dynamics, and system noises are improved due to the good accuracy of the time-series model. (3) Based on the designed estimation algorithm and sampled data, the time-series model includes multiple time-varying physical parameters of the system. (4) The orders of the model should be selected comprehensively, considering the permitted calculation time and performances to prevent overrun error or insufficient accuracy.
Wei Yao1,Ke Dongliang1,Huang Dongxiao1等. A Continuous-Control-Set Type Model-Free Predictive Current Control Based on Time-Series for Permanent Magnet Synchronous Motor Drives[J]. Transactions of China Electrotechnical Society, 2023, 38(22): 6027-6038.
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