A global optimization technique for modeling and control of permanent magnet synchronous motor drive

Abstract

In this paper, model order reduction and controller design of permanent magnet synchronous motor (PMSM) drive has been carried out with the help of a firefly-based hybrid metaheuristic algorithm in the complex delta domain. Two relatively new algorithms, namely, the firefly technique and an adaptive version of the flower pollination method are combined to develop an effective global optimization approach. Originally, the permanent magnet synchronous motor drive constituting speed and current controllers yields a higher-order system reduced to a lower-order model via an identification approach applied in signal processing techniques. The reduced-order model, cascaded with a PI controller is then matched with a reference model approximately to estimate the unknown controller parameters. The tuned controller parameters using the delta operator method almost resemble those obtained by the continuous-time system. Thus, a unified framework of controller design for the drive system is also established. Thus, the hybrid intelligent algorithm is employed for order reduction and controller parameter estimation of PMSM drives. A case study can also be considered for the speed control of switched reluctance and brushless motor drives are widely predominant in several domestic and industrial applications.