Intelligent High-Performance Interior Permanent Magnet Synchronous Motor Drive System(2/3)

The objective of this project is to develop an intelligent high-performance interior permanent magnet synchronous motor (IPMSM) drive system based on artificial intelligence using fuzzy neural network. To increase the performance of the IPMSM, the maximum torque per ampere (MTPA) control, field-weakening (FW) control and maximum voltage per voltage (MTPV) control will be developed. In the first year, the IPMSM drive system based on artificial intelligence using fuzzy neural network is developed and the identification of moment of inertia using a Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) is proposed. Moreover, in order to overcome the vibration of PMSM due to the poor coupling and mechanical friction, a resonance frequency detection system based on the discrete wavelet filter is also proposed in this year. Then, a band-pass filter (BPF) is adopted using sweep frequency to find out the resonance frequency of the system. Since the performance of IPMSM will vary nonlinearly owing to external influences such as temperature and magnetic saturation, the MTPA controller based on the recurrent Legendre fuzzy neural network (RLFNN) will be designed in the second year. The current angle command is obtained by the RLFNN to alleviate the effect of magnetic saturation. Furthermore, in the third year, the d-axis current command will be controlled to achieve the MTPA control first. Then, the FW control and MTPV control will be presented. In addition, the q-axis inductance is estimated by the RLFNN developed in the second year, and it is substituted into the formulas of MTPA and MTPV to alleviate the saturation effect. Additionally, an adaptive complementary sliding mode controller (ACSMC) will be developed in the speed control loop to improve the speed response.