Performance Of Sensorless Induction Motor Research Articles

Sensorless Sliding Mode Control of Induction Motor Using Fuzzy Logic Luenberger Observer

Many industrial applications require high performance speed sensorless operation and demand new control schemes in order to obtain fast dynamic response. In this paper, we present a speed sensorless sliding mode control (SMC) of induction motor (IM). The sliding mode control is a powerful tool to reject disturbances. However, the chattering phenomenon presents a major drawback for variable structure systems. To decrease this problem, a saturation function is used to limit chattering effects. A Luenberger observer based on fuzzy logic adaptation mechanism is designed for speed estimation. Numerical simulation results of the proposed scheme illustrate the good performance of sensorless induction motor and the robustness against load torque disturbances.

Online Stator and Rotor Resistance Estimation Scheme Using Sliding Mode Observer for Indirect Vector Controlled Speed Sensorless Induction Motor

Recently, many works have been made to improve the performance of sensorless induction motor drives. However, parameter variations and low-speed operations are the most critical aspects affecting the accuracy and stability of sensorless drives. This work presents a sensorless vector control scheme consisting of the first hand of a velocity estimation algorithm that overcomes the need for sensor velocity and secondly a robust variable structure control law that compensates for the uncertainties present in the system. Simulation results confirm the efficacy of the proposed approach.

A Speed and Flux Observer of Induction Motor Based on Extended Kalman Filter and Markov Chain

To improve the performance of sensorless induction motor (IM) drives, an adaptive speed and flux estimation method based on the multiple-model extended Kalman filter (EKF) with Markov chain for IMs is proposed in this paper. In this algorithm, the multiple model EKF for speed and flux estimation is established, and the transition of the models obeys the Markov chain and the estimation value is obtained by mixing the outputs of different models in different weightings, and the calculation of the weighting is researched. Simultaneously, the transition probability can be continuously self-tuned by the residual sequence, the prior information is modified by the posterior information, and the more accurate transition among the models is obtained. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results.

Evaluation of Speed and Torque Estimations for the EKF-based Direct Torque Control in Induction Machines

Accurate estimations of unmeasurable variables are required for increasing the performance of sensorless induction motor drives. This can be achieved if the required variables are accurately estimated with a well-established observer. The paper presents an extended Kalman filter- based direct torque control to investigate the estimations of speed and torque under challenging rotor and stator resistor and load variations at low and high speed regions. In all investigated scenarios, the speed and torque estimation showed good robustness against perturbations and their errors have remained within acceptable error bands.

Non-linear Direct Torque Control of Sensorless Induction Motor Drives with Parameter Identification and Capable for Very Low Speeds

The performance of sensorless induction motor drives is generally poor at extremely low speeds. The main recognized reasons are the limited accuracy of pulse-width modulation stator voltage acquisition, unwanted offset and drift components superimposed on the acquired measured signals, voltage distortions of induction motor caused by non-linear behavior of switching inverter, and finally, increased sensitivity against motor parameters mismatch. A sensorless induction drive capable of very low speed operation is presented in this article in which the principles of direct torque control and integrator backstepping non-linear control are combined to ensure high performance operation both in transient and steady state conditions. Stator flux vector is determined using a pure integrator in the stationary reference frame and is employed for rotor flux vector identification. An improved method is introduced for unavoidable offset and drift components identification and compensation. The average of stator phase voltages in each switching interval is estimated and employed in order to eliminate the AC voltage sensors. Feasible solutions are also proposed for on line stator and rotor resistances identification to ensure further improvement of drive performance at very low speeds. Effectiveness of the proposed control scheme is studied through simulation and experimental results.

A stable adaptive flux observer for a very low speed-sensorless induction motor drives insensitive to stator resistance variations

In recent years, numerous attempts have been made to improve the performance of sensorless induction motor drives. However, parameter variations, low-speed and zero-speed operations are the most critical aspects affecting the accuracy and stability of sensorless drives. This paper presents a stable adaptive flux observer (AFO) for sensorless induction motor drives insensitive to stator resistance variations. Design of the observer feedback gain of AFO is proposed to guarantee the stability, especially at low speeds. The adaptive law parameters are designed to give quick transient response and good tracking performance. The sensitivity of AFO to stator resistance mismatch is studied. A stator resistance adaptation scheme for accurate speed estimation at low speeds is derived. The relation between the identification error of rotor speed and adaptive gains is clarified based on Lyapunov theory. Experimental setup using DSP-DS1102 control board is implemented. Simulation and experimental results confirm the efficacy of the proposed approach.