00000cab a22000003a 4500 UP-99796217609532858 Buklod 20231007234345.0 m |o d | ta 101109s xx d | ||r ||||| || DENGII eng Faa-Jeng Lin Hybrid control using recurrent fuzzy neural network for linear induction motor servo drive. pp. 102-115 A hybrid control system using a recurrent fuzzy neural network (RFNN) is proposed to control a linear induction motor (LIM) servo drive. First, feedback linearization theory is used to decouple the thrust force and the flux amplitude of the LIM. Then, a hybrid control system is proposed to control the mover of the LIM for periodic motion. In the hybrid control system, the RFNN controller is the main tracking controller, which is used to mimic a perfect control law, and the compensated controller is proposed to compensate the difference between the perfect control law and the RFNN controller. Moreover, an online parameter training methodology, which is derived using the Lyapunov stability theorem and the gradient descent method is proposed to increase the learning capability of the RFNN. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results. Furthermore, the advantages of the proposed control system are indicated in comparison with the sliding mode control system Lyapunov stability theorem. Feedback linearization theory. Flux amplitude. Gradient descent method. Hybrid control. Learning capability. Linear induction motor servo drive. Online parameter training methodology. Perfect control law. Recurrent fuzzy neural network. Thrust force. Tracking controller. IEEE Transactions on fuzzy systems 9, 1 (2001). FO UPD DENG-II Article