Sensorless Control Strategies for Brushless DC Motor Drives
Summary
Sensorless control of brushless DC (BLDC) motors has emerged as a pivotal advancement in drive technology, eliminating the need for physical position or speed sensors and thereby reducing system cost, complexity and susceptibility to environmental disturbances. At its core, sensorless operation relies on the measurement or estimation of electrical variables—most commonly the back electromotive force (back-EMF) or current waveforms—to infer rotor position and velocity. Various observer architectures, ranging from model-based approaches such as extended Kalman filters and disturbance observers to data-driven methods using neural networks, have been developed to deliver high-precision estimation across the entire speed range. Complementary strategies, including predictive control and specialised modulation schemes, serve to mitigate commutation torque ripple and enhance dynamic performance. These innovations have not only driven improvements in industrial automation, electric vehicles and household appliances, but have also broadened the application envelope of BLDC drives to harsh or cost-sensitive environments where conventional sensors would be impractical or unreliable.
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Sensorless Control Strategies for Brushless DC Motor Drives publication trend
The graph below shows the total number of articles in sensorless control strategies for brushless dc motor drives across all publications each year (not limited to Nature Index journals).
Technical terms
Back electromotive force (back-EMF): Voltage induced in motor windings by rotor magnets, used to infer rotor position when unmeasured.
Disturbance observer: Model-based estimator that reconstructs unknown perturbations and system states from measured inputs and outputs.
Model predictive control: Control strategy that optimises future control moves by solving a constrained optimisation problem at each sampling instant.
Neural network: Data-driven algorithm composed of interconnected processing nodes, capable of learning nonlinear mappings from input features to target estimates.
Commutation torque ripple: Periodic variation in electromagnetic torque during phase switching, leading to noise and mechanical vibration.
References
- Position and Speed Control of Brushless DC Motors Using Sensorless Techniques and Application Trends. Sensors (2010).
- Iron-Loss Modeling With Sensorless Predictive Control of PMBLDC Motor Drive for Electric Vehicle Application. IEEE Transactions on Transportation Electrification (2020).
- ANN-based position and speed sensorless estimation for BLDC motors. Measurement (2022).
- Sensorless Speed Tracking of a Brushless DC Motor Using a Neural Network. Mathematical and Computational Applications (2020).
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