# Position and Speed Control of Brushless DC Motors Using Sensorless Techniques and Application Trends

**Authors:** Jose-Carlos Gamazo-Real, Ernesto Vazquez-Sanchez, Jaime Gomez-Gil  
**arXiv:** [2402.05263](https://arxiv.org/abs/2402.05263)  
**Date:** 2024-02-07  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2402.05263v1/

## Key points

- Comprehensive review of sensorless methods for controlling BLDC motor drives
- Analysis of the use of sensors and limitations of traditional control techniques
- Advancements in performance and reliability through sensorless technology
- Overview of state-of-the-art back-EMF sensing methods (Terminal Voltage Sensing, Third Harmonic Voltage Integration, Terminal Current Sensing, Back-EMF Integration, PWM strategies)
- Analysis of estimation and model-based techniques (Sliding-mode Observer, Extended Kalman Filter, Model Reference Adaptive System, Adaptive observers - Full-order and Pseudoreduced-order, Artificial Neural Networks)
- Advancements in position control by eliminating traditional sensors like shaft encoders or Hall-effect probes
- Importance of sensorless control for applications with harsh environmental conditions requiring higher performance levels

## Summary

This paper presents a comprehensive review of sensorless methods for controlling Brushless Direct Current (BLDC) motor drives. The focus is on analyzing the use of sensors and the limitations of traditional control techniques, as well as advancements in performance and reliability through sensorless technology. It delves into recent developments in the field, discussing their advantages and drawbacks, practical implementation issues and applications. The paper provides an overview of state-of-the-art back-EMF sensing methods such as Terminal Voltage Sensing, Third Harmonic Voltage Integration, Terminal Current Sensing, Back-EMF Integration, and PWM strategies. It also briefly analyzes estimation and model-based techniques like Sliding-mode Observer, Extended Kalman Filter, Model Reference Adaptive System, Adaptive observers (Full-order and Pseudoreduced-order), and Artificial Neural Networks. Furthermore, it discusses advancements in position control by eliminating traditional sensors like shaft encoders or Hall-effect probes to reduce costs and enhance reliability. This highlights the importance of sensorless control for applications where traditional sensors may not function reliably due to harsh environmental conditions while requiring higher performance levels. Overall,. It offers insights into existing methods as well as newer approaches with their respective benefits and limitations to guide future research efforts in this area.