Intelligent backstepping control for linear induction motor drive

The design and properties of an intelligent backstepping control system for a linear induction motor (LIM) drive to track periodic reference trajectories are studied. First, the dynamic model of a field-oriented control LIM drive is derived. Then a feedback linearisation controller is designed in the sense of the backstepping control technique. To relax the requirement for the bound of lumped uncertainty in the feedback linearisation control law, a recurrent fuzzy neural network (RFNN) uncertainty observer is proposed to estimate the lumped uncertainty in real time. In addition, an online parameter training methodology, derived using the Lyapunov stability theorem and the gradient descent method, is proposed to increase the learning capability of the RFNN. With the proposed intelligent backstepping control system, the mover position of the LIM drive possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic reference trajectories. The effec tiveness of the proposed control scheme is verified by both simulated and experimental results.