Sensorless and Adaptive Control of Induction Machines

Dear Colleagues,

Induction machines have been widely utilized in industries for decades due to their easy and robust construction as well as their cost-effectiveness. Their simplicity and affordability make them a preferred choice in numerous applications. Achieving dynamic variable speed control of induction motors is made possible through inverter-driven vector control. However, this approach necessitates knowledge of the rotor position, typically obtained by using sensors. Unfortunately, these sensors are rather expensive and delicate electronic devices.

A more resilient alternative is utilizing the induction machine itself as a rotor position sensor, an idea known as "sensorless control." Numerous methods have been proposed and demonstrated promising practical results. In the case of medium- to high-speed applications, many of these sensorless position estimation techniques have been implemented in commercial variable speed drives. However, achieving sensorless control of induction motors in the very low- and zero-speed regions remains a significant research challenge.

Several sensorless techniques employ model-based observers, which are highly sensitive to parameter deviations. Adaptive parameter estimation techniques prove to be immensely helpful in maintaining observer parameters as precise as possible across all motor operation conditions. Other sensorless techniques utilize high-frequency or transient pulse signal responses to estimate magnetic saliencies in the machine rotor. The obtained saliency is typically influenced by the rotor bar slots of the induction rotor cage and modulation due to saturation caused by the motor currents. This requires sophisticated modulation signal separation. Additionally, non-linearities in the machine signals present further challenges.

This Special Issue aims to gather new research results in the domain of sensorless and adaptive control of induction motors.

Research topics that are of interest for this Special Issue include but not limited to the following:

• Signal injection methods to detect induction motor magnetic saliencies;
• Model-based sensorless position estimation based on machine flux/back EMF;
• Hybrid sensorless techniques using back EMF models and magnetic saliency detection;
• Adaptive parameter estimation of induction machines;
• Non-linear parameter modelling of induction machines;
• Pattern recognition of saliency modulation;
• rotor position reconstruction from measured saliencies;
• Filtering and signal processing of estimates position signals.

Dr. Reiko Raute
Guest Editor

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• induction motor
• variable speed drives
• inverters
• sensorless control
• adaptive control
• parameter estimation

Title: Stator Current-Based Model Reference Adaptive Control for Sensorless Speed Control of the Induction Motor
Authors: Workagegn Tatek Asfu
Affiliation: Department of Electrical and Computer Engineering，Debre Berhan University，Debre Berhan, Ethiopia
Abstract: Abstract-Torque produced in IM (induction Motor) is collected fundamental torque, however, due to core saturation, air gap irregularity, and winding distribution; stator and rotor slotting harmonics torque are produced. This reduces the quality of the power system, life span, and performance of the motor and the controller device. In this paper, a neural network, based fractional order proportional integral derivative (NNFOPID) controller is designed to compensate the harmonics of the induction motor driving system. FOPID controller parameters have been tuned automatically based on the delta-learning rule with sigmoid activation function. The active shunt capacitor is directly connected to five- level three-phase inverter directly controlled by sinusoidal pulse width modulation (SPWM). Based on the reference and measured harmonics, error the controller parameter of FOPID is tuned using NN (neural network) delta learning algorithm. The design of discrete type PI speed and current vector controller is followed by space vector pulse width modulation (SVPWM) is deigned to track the actual speed of IM to the reference speed. The IM modeling and power electronics driving the system within its harmonics effect was analyzed and discussed. In addition, the effect of current and voltage harmonics with different conditions is illustrated. For this NNFOPID controller parameter a tuning was designed and Matlab simulations check the system performance. The result shows that the current harmonic and voltage harmonic were reduced to 2.79%, 12.12%, respectively.Index Terms—Neural network, Fractional order PID, Vector control, five level three phase inverter