基于MRAS和FNN的异步电动机无速度传感器直接转矩控制

Speed Sensorless Direct Torque Control System of Induction Motor Based on MRAS and FNN

针对传统直接转矩控制系统中磁链和转矩存在较大的脉动,采用基于模糊神经网络(FNN)控制器的预期电压矢量调制方案。速度传感器安装不方便、成本高,磁链观测对电阻的依赖性强,尤其是在电机低速运行时,应用模型参考自适应控制策略(MRAS)设计速度、电阻自适应定子磁链观测器,以定子电流和定子磁链为状态变量,利用Popov超稳定性理论得到转子转速和转子电阻的自适应律。建立了异步电动机无速度传感器直接转矩控制系统的Matlab/Simulink仿真模型,仿真结果表明系统脉动较小,提高了定子磁链和转速的观测精度。

Due to the large pulsation of torque and flux in traditional direct torque control system, the anticipated voltage vector modulation technique was used based on FNN control. Speed sensor was inconvenient and expensive and the flux observer heavily depended on resistance, especially in low speed. Thus, a speed, resistance and flux adaptive observer was designed based on model reference adaptive control theory. The stator currents and stator fluxes were used as state variables, while the rotor speed and the rotor resistance law were defined by Popov hyperstability theory. The speed sensodess direct torque control system was established under Matlab/Simulink. The results showed that the fluctuation of the torque and flux was smaller and the stator flux and the rotor speed were observed more exactly.