25권 2호 213-224 2024 [IJAT]
| 제목 | Fractional order complementary non-singular terminal sliding mode control of PMSM based on neural network |
|---|---|
| 분야 | Electric, Fuel Cell, and Hybrid Vehicle |
| 언어 | English |
| 저자 | Jinliang Zhang(Hubei University of Automotive Technology), Dunbin Zhu(Hubei University of Automotive Technology), Wei Jian(Hubei University of Automotive Technology), Wentao Hu(Hubei University of Automotive Technology), Guosheng Peng(Hubei University of Automotive Technology), Yufeng Chen(Hubei University of Automotive Technology), Zhihu Wang(Dongfeng Automobile Power Parts Co. Ltd) |
| Key Words | Permanent magnet synchronous motor · Fractional order complementary · Neural network · Electric vehicle drive system · Non-singular terminal sliding mode |
| 초록 |
Aiming at the sensitivity problems of uncertain factors such as parameter variation, external disturbance and friction for the permanent magnet synchronous motor control system of electric vehicle, a fractional order complementary non-singular terminal sliding mode control method based on neural network is proposed. The mathematical model of permanent magnet synchronous motor with uncertain factors was established. The sliding mode controller was designed by combining the generalized sliding mode surface and the complementary sliding mode surface, which shortened the arrival time from the state trajectory to sliding mode surface. The fractional calculus operator with fi ltering characteristics was used to improve the position tracking accuracy and reduce the chattering. As for the variety of uncertain disturbances, the neural network was used to estimate the system total uncertainty and compensate online to further improve the dynamic response ability and anti-interference ability. Finally, the simulation results verify the eff ectiveness and feasibility of the proposed method, which can provide theoretical and technical support for improving the control accuracy of permanent magnet synchronous motor and the development of electric vehicles. |
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