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| Model-Free Predictive Sliding Mode Control Using Ultra-Localized Time-Series for Permanent Magnet Synchronous Motor Drives |
| Wei Yao1, Ke Dongliang1, Huang Dongxiao1, Wang Fengxiang1, Zhang Zhenbin2 |
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. School of Electrical Engineering Shandong University Jinan 250061 China |
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Abstract The limited flexibility and robustness of the typical sliding mode control (SMC) fail to meet the demands of complex environments with variable loads and the influence of time-varying inductance parameters, magnetic field coupling, core saturation, and other factors. On the contrary, the model-free SMC strategy is more effective. This paper proposes a model-free predictive SMC strategy utilizing an ultra-localized time-series model for a permanent magnet synchronous motor (PMSM) driving system. By representing the motor as a collection of discrete-time linear functions and maintaining high model accuracy through an online estimation algorithm, the proposed strategy is better aligned with the motion characteristics of the motor system.
Firstly, this approach establishes an ultra-localized time-series model and updates the regressive vector, which only summarizes input and output signals based on sampled data. Secondly, all undetermined coefficients in the model are estimated using the recursive least square (RLS) algorithm. Consequently, the current operating state of the motor driving system is described as a collection of discrete-time linear functions and converted into the ultra-local structure to generate the sliding mode signal. Finally, control functions are designed based on the power-reaching rule, and the reaching conditions are verified using the Lyapunov method. This ultra-localized time-series model is easily implemented within the SMC strategy, offering good accuracy and addressing the issues caused by time-varying physical parameters in the plant model of the typical SMC and input gain of the conventional observer-based ultra-local model.
Simulation and experimental results on a PMSM driving system demonstrate the effectiveness of the proposed method in resisting disturbances and successfully tracking the reference. The disturbances primarily include changed parameter mismatches and load torque. Fourier analysis and accumulated error comparisons between the proposed and conventional model-free SMC strategies show that the proposed method reduces the total harmonic distortion (THD) by 3.14%. Compared to the conventional strategy, the proposed method exhibits the minimum ascending slope of accumulated error for current and lower operating noise amplitudes in various speed references and load torques. Experimental results with different parameter mismatches of the stator inductance further verify its robustness.
The validations provide the following conclusions: (1) The proposed method adopts an ultra-localized time-series model to represent the current operating state of the motor driving system. Unlike the conventional strategy that utilizes an ultra-local approach, this model formulates the system as a collection of discrete-time linear functions. (2) The ultra-localized time-series model significantly improves current quality, accumulated current error, and system noise. This improvement is attributed to the high accuracy of the ultra-localized time-series model and the eliminated influences of the unsuitable input gain. (3) By employing a designed estimation algorithm and sampled data, the ultra-localized time-series model replaces the physical model, which involves multiple time-varying physical parameters, enabling more accurate modeling and updating processes.
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Received: 16 March 2023
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2024, Vol. 39 
