Indirect field-oriented linear induction motor drive with Petri fuzzy-neural-network control

Abstract

This study focuses on the development of a Petri fuzzy-neural-network (PFNN) control for an indirect field-oriented linear induction motor (LIM) drive. The concept of a Petri net (PIN) is incorporated into a traditional fuzzy-neural-network (TFNN) to form a newly-type PFNN framework for alleviating the computation burden. Moreover, the supervised gradient descent method is used to develop the online training algorithm for the PFNN. In order to guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the PFNN. With the proposed PFNN control system, the mover position of the controlled LIM drive possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic reference trajectories. In addition, the effectiveness of the proposed control scheme is verified by numerical simulations.