基于时间序列的永磁同步电机连续控制集无模型预测电流控制

doi:10.19595/j.cnki.1000-6753.tces.230159

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Abstract

The finite-control-set type (FCS-type) predictive control method is not ideal for electric vehicles because 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) using time-series model describes the motor as a discrete transfer function, which is more compliant with the motion characteristics of the motor system. However, the difficulty of calculating the inverse of the model in the digital processor makes it challenging to apply in the CCS-type. To address this issue, a time-series-based CCS-type MF-PCC strategy is proposed in this paper for a permanent magnet synchronous motor (PMSM) driving system. By the online building and updating time-series model based on the sampled data, the plant is accurately expressed, and the control strategy is easily realized in the CCS-type.
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. This way, 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 fully 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, in which 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. These results demonstrate the effectiveness of the time-series model and the convergence of the estimation algorithm. Additionally, to analyze the impact of system noise on driver perception, the proposed method is tested under different speed references and load torques. Finally, to validate the achieved robustness, 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 from the validations: 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) Compared with conventional MF-PCC strategies using ultra-local, the obtained current quality, dynamics and system noises are obviously improved due to the good accuracy of the time-series model. 3) The time-series model replaces the a priori model, which includes multiple time-varying physical parameters of the system, since the modeling and updating processes of the time-series model are directly based on the designed estimation algorithm and sampled data. 4) The orders of the model should be selected comprehensively, considering the permitted calculation time and performances to prevent overrun error or insufficient accuracy.

Key words： Model-free predictive control time series model least square estimation algorithm current prediction

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TM341

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	Yao Wei
	Dongliang Ke
	Dongxiao Huang
	Fengxiang Wang
	Jinsong Kang

Cite this article:

Yao Wei,Dongliang Ke,Dongxiao Huang等. A Continuous-Control-Set Type Model-Free Predictive Current Control based on Time-Series for PMSM Drives[J]. Transactions of China Electrotechnical Society, 0, (): 8921-.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.230159 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/8921

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