Hybrid Neural-Network Model-Following Speed Controller with On-Line Learning for Vector-Controlled PMSM Drive

Abstract:- A high-performance robust hybrid speed controller of permanent-magnet synchronous motor (PMSM) drive with on-line trained neural-network model-following controller (NNMFC) is proposed. The robust hybrid controller is a two-degrees-of-freedom (2DOF) integral plus proportional & rate feedback (I-PD) with neural-network model-following (NNMFC) speed controller (2DOF I-PD NNMFC). The robust controller combines the merits of the 2DOF I-PD controller and the NNMFC controller for PMSM drives speed control. First, a systematic mathematical procedure is derived to find the parameters of the d-q axes PI current controllers and the 2DOF I-PD speed controller according to the required specifications for the PMSM drive system. Then the resulting closed loop transfer function of the PMSM drive system including the current control loop is used as the reference model. To realize high dynamic performance in disturbance rejection and tracking characteristics, a neural-network model-following controller whose weights are trained on-line is designed in addition to the 2DOF I-PD controller. According to the model-following error between the outputs of the reference model and the PMSM drive system, the NNMFC generates an adaptive control signal which is added to the 2DOF I-PD speed controller output to attain robust model-following characteristics under different operating conditions due to parameter variations and load disturbances. Computer simulation is developed to demonstrate the effectiveness of the proposed 2DOF I-PD NNMFC controller. The results confirm that the proposed 2DOF I-PD NNMFC speed controller grant a rapid, robust performance and accurate response for the reference model regardless of whether a load disturbance is imposed and PMSM parameters vary