System identification: theory for the user
System identification: theory for the user
Feedback linearization using neural networks
Automatica (Journal of IFAC)
Nonlinear and Adaptive Control Design
Nonlinear and Adaptive Control Design
Indirect vector control of induction motor
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Position predictive control for an induction motor
CONTROL'07 Proceedings of the 3rd WSEAS/IASME international conference on Dynamical systems and control
Multilayer neural-net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
Some new results on system identification with dynamic neural networks
IEEE Transactions on Neural Networks
Design of a neuro-controller for multivariable nonlinear time-varying systems
WSEAS Transactions on Systems and Control
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A new control approach is proposed to address the tracking problem of an induction machine based on a modified field-oriented control (FOC) method. In this approach, one relies first on a partially known model to the system to be controlled using a backstepping control strategy. The obtained controller is then augmented by an online neural network that serves as an approximator for the neglected dynamics and modeling errors. The proposed approach is systematic, and exploits the known nonlinear dynamics to derive the stepwise virtual stabilizing control laws. At the final step, an augmented Lyapunov function is introduced to derive the adaptation laws of the network weights. The effectiveness of the proposed controller is demonstrated through computer simulation.