Robust asymptotic stability of uncertain fuzzy BAM neural networks with time-varying delays
Fuzzy Sets and Systems
Global exponential stability analysis of fuzzy BAM neural networks with time-varying delays
International Journal of Hybrid Intelligent Systems
Information Sciences: an International Journal
Brief paper: Direct adaptive fuzzy control of nonlinear strict-feedback systems
Automatica (Journal of IFAC)
Multiobjective algebraic synthesis of neural control systems by implicit model following
IEEE Transactions on Neural Networks
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Adaptive fuzzy logic based control of induction motors
Control and Intelligent Systems
IEEE Transactions on Neural Networks
Radial basis function neural network-based adaptive critic control of induction motors
Applied Soft Computing
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We present a new robust control technique for induction motors using neural networks (NNs). The method is systematic and robust to parameter variations. Motivated by the backstepping design technique, we first treat certain signals in the system as fictitious control inputs to a simpler subsystem. A two-layer NN is used in this stage to design the fictitious controller. We then apply a second two-layer NN to robustly realize the fictitious NN signals designed in the previous step. A new tuning scheme is proposed which can guarantee the boundedness of tracking error and weight updates. A main advantage of our method is that it does not require regression matrices, so that no preliminary dynamical analysis is needed. Another salient feature of our NN approach is that the off-line learning phase is not needed. Full state feedback is needed for implementation. Load torque and rotor resistance can be unknown but bounded