基于自适应模糊神经网络的感应电机矢量控制

doi:10.16180/j.cnki.issn1007-7820.2022.09.010

Abstract

Abstract:

In view of the problem that the induction motor has fixed parameters and is easy to overshoot in the traditional PI control, a method based on the adaptive fuzzy neural network PI control and the full-order adaptive observer is proposed in this study. According to the mathematical model of the induction motor, the model of the full-order adaptive observer is established, and the stability analysis and design are carried out using the Lyapunov stability theory, and the speed adaptive law is deduced. The motor speed outer loop PI is adjusted and optimized online by an adaptive fuzzy neural network inference system. Compared with traditional control schemes, this method is easy to implement, can effectively improve control accuracy, suppress external disturbances, and save sensor costs. MATLAB/Simulink simulation experiments show that the proposed scheme not only improves the dynamic performance of the speed sensorless induction motor vector control system, but also reduces the influence of external load disturbances, and improves the system's adaptability and robustness.

Key words: induction motor, no speed sensor, vector control, PI controller, ANFIS, full-order adaptive observer, online tuning, robustness

CLC Number:

TN787

JIN Aijuan,SHAO Feixuan,YAN Ziguang. Vector Control of Induction Motor Based on Adaptive Fuzzy Neural Network[J].Electronic Science and Technology, 2022, 35(9): 65-73.

Figures/Tables 11

Figure 1.

Figure 2.

Table 1.

Motor parameters"

参数	数值
定子电阻/R_s	1.115
转子电阻/R_r	1.083
定子漏感/ $L l s$	0.005 974
转子漏感/ $L l r$	0.005 974
定转子互感/L_m	0.203 7
级对数/P_n	2
转动惯量/J	0.02
摩擦系数/N·m·s	0.005 752

Table 1.

Figure 3.

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Figure 9.

Figure 10.

References 19

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Vector Control of Induction Motor Based on Adaptive Fuzzy Neural Network

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