Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
A model reference control structure using a fuzzy neural network
Fuzzy Sets and Systems
Adaptive Control
Control of Electrical Drives
A Recurrent Fuzzy-Network-Based Inverse Modeling Method for a Temperature System Control
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On-line tuning of fuzzy-neural network for adaptive control of nonlinear dynamical systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A supervisory fuzzy neural network control system for tracking periodic inputs
IEEE Transactions on Fuzzy Systems
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
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The hybrid recurrent fuzzy neural network (HRFNN) control for permanent magnet synchronous motor (PMSM) drive system using rotor flux estimator is developed to control electric scooter in this paper. First, the dynamic models of a PMSM drive system were derived in according to electric scooter. Owing to the load of electric scooter exited many uncertainties, for example, nonlinear friction force of the transmission belt, and so forth. The electric scooter with nonlinear uncertainties made the PI controller to disable speed tracking control. Moreover, in order to reduce interference of encoder and cost down, an HRFNN control system using rotor flux estimator was developed to control PMSM drive system in order to drive electric scooter. The rotor flux estimator consists of the estimation algorithm of rotor flux position and speed based on the back electromagnetic force (EMF) in order to supply with HRFNN controller. The HRFNN controller consists of the supervisor control, RFNN, and compensated control with adaptive law is applied to PMSM drive system. The parameters of RFNN are trained according to different speeds in electric scooter. The electric scooter is operated to provide disturbance torque. To show the effectiveness of the proposed controller, comparative studies with PI controller are demonstrated by experimental results.