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Vector Control of Faulty Three-Phase Induction Motor with an Adaptive Sliding Mode Control

Jannati M., Monadi A., Idris N. R., Aziz M. J. A., Faudzi A. A. M.

Przegląd Elektrotechniczny

2013

R. 89, nr 12

116-120

This paper presents an adaptive sliding mode control with an adaptive switching gain in order to vector control of Three-Phase Induction Motor (TPIM) based on Rotor Flux Oriented Control (RFOC) method under open-phase fault (faulty TPIM). This method can be utilized to control of IMs in some critical applications which require fault-tolerant scheme. To confirm the good performance of the proposed method, simulation results have been presented. The Simulation results confirm a completely satisfactory effectiveness of the proposed method.

W artykule zaprezentowano adaptacyjne sterowanie ślizgowe trójfazowego silnika indukcyjnego bazujące na metodzie RFOC w przypadku gdy jedna z faz wykazuje błąd.

Recurrent neural identification and control of a continuous bioprocess via first and second order learning

Baruch I., Mariaca-Gaspar C. R.

Journal of Automation Mobile Robotics and Intelligent Systems

2010

Vol. 4, No. 4

37-52

This paper applies a new Kalman Filter Recurrent Neural Network (KFRNN) topology and a recursive Levenberg-Mar quardt (L-M) learning algorithm capable to estimate para meters and states of highly nonlinear unknown plant in noisy environment. The proposed KFRNN identifier, learned by the Backpropagation and L-M learning algorithm, was incorporated in a direct and indirect adaptive neural con trol schemes. The proposed control schemes were applied for real-time recurrent neural identification and control of a continuous stirred tank bioreactor model, where fast convergence, noise filtering and low mean squared error of reference tracking were achieved.