This paper deals with an analysis of the vector-controlled induction-motor (IM) drive with a novel model reference adaptive system (MRAS)-type rotor speed estimator. A stability-analysis method of this novel MRAS estimator is shown. The influence of equivalent-circuit parameter changes of the IM on the pole placement of the estimator transfer function and the stability of the whole drive system are analyzed and tested. The influence of the adaptation-algorithm coefficients of the MRAS-estimator scheme is also tested. The allowable range of motor-parameter changes is determined, which guarantees the stable operation of the sensorless field-oriented IM drive with this speed and flux estimator. Dynamical performances of the vector-control system with the current-type MRAS estimator are tested in a laboratory setup.

In this paper, the concept of a model reference adaptive control of a sensorless induction motor (IM) drive with elastic joint is proposed. An adaptive speed controller uses fuzzy neural network equipped with an additional option for online tuning of its chosen parameters. A sliding-mode neuro-fuzzy controller is used as the speed controller, whose connective weights are trained online according to the error between the estimated motor speed and the speed given by the reference model. The speed of the vector-controlled IM is estimated using the MRAS^CC rotor speed and a flux estimator. Such a control structure is proposed to damp torsional vibrations in a two-mass system in an effective way. It is shown that torsional oscillations can be successfully suppressed in the proposed control structure, using only one basic feedback from the motor speed given by the proposed speed estimator. Simulation results are verified by experimental tests over a wide range of motor speed and drive parameter changes.

This paper describes a Fault Tolerant Control structure for the Induction Motor (IM) drive. We analyzed the influence of current sensor faults on the properties of the vector-controlled IM drive system. As a control algorithm, the Direct Field Oriented Control structure was chosen. For the proper operation of this system and for other vector algorithms, information about the stator currents components is required. It is important to monitor and detect these sensor faults, especially in drives with an increased safety level. We discuss the possibility of the neural network application in detecting stator current sensor faults in the vector control algorithm. Simulation and experimental results for various drive conditions are presented.

In this paper, an adaptive control strategy for the field oriented controlled induction motor (IM) drive system with elastic joint is presented. The changeable inertia of the load machine is estimated by the nonlinear extended Kalman filter. Then, the control structure parameters are returned in accordance with the estimated value. The provided experimental results for the IM drive confirm the effectiveness of the proposed strategy.

This paper describes a vector-controlled Permanent Magnet Synchronous Motor (PMSM) drive system with the current sensor fault detection mechanism. In general, the control structure is based on the well-known Field Oriented Control (FOC) algorithm. The structure is equipped with an additional algorithm for current sensor fault detection based on a neural network. The presented control structure is able to detect typical current sensor faults, such as lack of signal, intermittent signal, variable gain and signal noise. The application of the NN detector guarantees a faster detection of the sensor fault than classical detectors based on algorithmic methods or logical systems. This work focuses on presenting the methodology of designing detectors and their analysis, based solely on simulation analysis. The simulation results, conducted in the Matlab/Simulink environment, are presented for the above-mentioned faults in phase A and phase B for different speed conditions.