Neural methods for antenna array signal processing: a review
Signal Processing
A self-organizing feature map-driven approach to fuzzy approximate reasoning
Expert Systems with Applications: An International Journal
Neural identification and control for linear induction motors
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
A comparative study of adaptive fuzzy control schemes for induction motor drives
ACMOS'05 Proceedings of the 7th WSEAS international conference on Automatic control, modeling and simulation
A hybrid neuro-fuzzy approach for spinal force evaluation in manual materials handling tasks
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Engineering Applications of Artificial Intelligence
A hybrid neuro-fuzzy controller for brushless DC motors
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
Recurrent wavelet-based neuro fuzzy networks for dynamic system identification
Mathematical and Computer Modelling: An International Journal
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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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