Inertia Identification and Speed Control in Electrical Drive Systems
Summary
Inertia identification and speed control lie at the heart of modern electrical drive performance, determining both dynamic responsiveness and energy efficiency. Precise knowledge of the rotor and load inertia allows control algorithms to predict acceleration behaviour and compensate for disturbances, while accurate speed regulation ensures stability under variable loads. Advances in real-time parameter estimation have transformed conventional approaches, enabling adaptive and sensorless schemes that maintain high‐performance tracking even in the absence of direct measurements. Techniques such as recursive estimation, Kalman observers and current-ripple analysis have been integrated into cascaded and predictive control architectures to deliver robust speed regulation across diverse drive types, from brushless DC motors to permanent-magnet synchronous machines. The global push towards electrification of transport, automation and renewable energy conversion has heightened the demand for compact, efficient and fault-tolerant drive systems. As a result, contemporary research blends model-based inference with data-driven adaptation, achieving inertia identification on the fly and securing precise speed control in harsh or sensor-limited environments.
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Inertia Identification and Speed Control in Electrical Drive Systems publication trend
The graph below shows the total number of articles in inertia identification and speed control in electrical drive systems across all publications each year (not limited to Nature Index journals).
Technical terms
Moment of inertia: Quantitative measure of an object’s resistance to change in rotational speed.
Load torque: Rotational force exerted by the driven load opposing motor motion.
Kalman observer: Recursive filter that estimates system states and parameters by combining model predictions with noisy measurements.
Recursive least squares: Online parameter estimation algorithm that minimises the accumulated squared prediction error.
Sensorless control: Technique for regulating speed without physical speed or position sensors, relying on electrical signal inference.
Current ripple component: High-frequency fluctuation in motor current arising from commutation, used for speed estimation.
Adaptive control: Strategy that adjusts controller parameters in real time to accommodate system uncertainties and variations.
References
- An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares. Energies (2018).
- Sensorless Speed Control of Brushed DC Motor Based at New Current Ripple Component Signal Processing. Energies (2021).
- Intelligent Parameter Identification for Robot Servo Controller Based on Improved Integration Method. Sensors (2021).
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