In advanced motor control system, an accurate knowledge of motor parameters is essential in order to achieve a good performance. Some of these parameters, such as stator resistance are given by constructors, but they vary in different operating conditions. The purpose of this paper is to present an efficient dynamical identification method to estimate parameters of a permanent magnet synchronous machine (PMSM). Using this method, we can determine these parameters during different operating conditions. This method has been simulated and implemented in an experimental test bench using a dSpace board and obtained results proved the validity and efficiency of proposed method. Physical parameters estimation of a dynamical 3-parameter model thanks to dynamical measurements is performed using an output error algorithm based on non linear optimisation. Simulations results show that, if PMSM is fed by a PWM inverter, no extra-signal, such as a pseudo random binary sequence (PRBS) adding on speed reference in vector control of PMSM, is necessary to excite the machine to assure identification algorithm convergence. However an extra-signal is a disturbance in respect to the machine running.

This paper introduces a generalized predictive controller (GPC) for the speed control of a PMSM. The performance of the proposed controller is evaluated in simulation and compared with a IP controller. The stator flux linkage and electromagnetic torque are controlled by (DTC) strategy. For the speed regulation, the generalized predictive control strategy is used. Simulation results indicate that the proposed (GPDTC) is reliable and effective for the speed control of the PMSM over a wide range of operations of the PMSM drive.

In advanced motor control systems, an accurate knowledge of motor parameters is essential in order to achieve good performances. Some of these parameters, such as stator resistances, are sometimes given by constructors, but they vary according to the operating conditions. This paper presents and compares two parameter identification methods of an actuator used in haptic interfaces which, in this case, is a permanent magnet synchronous machine (PMSM). Using these methods, the electrical parameters of the PMSM can be determined during different operating conditions. Physical parameters estimation of a dynamic 4-parameter model is performed on the one hand, using an output error identification method based on a non linear programming algorithm and on the other hand, using an identification method based on least-squares techniques and inverse models

The numerous qualities of induction machines enhance their use in industry. However, various faults can occur, such as stator short-circuits and rotor failures. We focus our study on the latter since the unknown evolution of rotor currents in the squirrel cage makes the detection of the rotor's failures very difficult. In this paper, we present the effects of the rotor failures by multi-windings model simulation and we use a technique based on the spectral analysis of stator current in order to detect a breakdown or a defect in the rotor. Thus, the number and the position effect of rotor bars have been studied and a comparison between simulation and experimental results has been realized. The effect is highlighted by considering the machine supplied through a balanced three phase voltage network for the simulation as well as for the experimental tests.