# Position Sensor-less and Adaptive Speed Design for Controlling Brush-less DC Motor Drives

**Authors:** S. Geraee, M. Shafiei, A. R. Sahami, S. Alavi  
**arXiv:** [1808.06768](https://arxiv.org/abs/1808.06768)  
**Date:** 2018-08-21  
**Summary page:** https://www.summarizepaper.com/en/paper/1808.06768v1/

## Key points

- Novel method for direct torque control of Brushless DC (BLDC) motors
- Use of sliding mode observer to evaluate back-EMF with only one measurement of stator current
- Employment of Recursive Least Square (RLS) for real-time estimation of inertia and viscous damping coefficients
- Implementation of Modified Model Reference Adaptive System (MMRAS) for designing a new speed controller
- Validation through simulation results showing effectiveness in controlling BLDC motor drives without position sensors, adapting to varying speeds efficiently

## Summary

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The paper "Position Sensor-less and Adaptive Speed Design for Controlling Brush-less DC Motor Drives" by S. Geraee, M. Shafiei, A. R. Sahami, and S. Alavi presents a novel method for direct torque control of Brushless DC (BLDC) motors. The proposed approach involves using a sliding mode observer to evaluate the trapezium of back-EMF with only one measurement of stator current, effectively reducing the impact of switching noise and eliminating the need for additional filtering. To address uncertainties inherent in BLDC motors, Recursive Least Square (RLS) is employed as a real-time estimator to determine inertia and viscous damping coefficients. This allows for accurate calculation of rotor speed by incorporating estimated load torque into mechanical dynamic equations. Furthermore, to enhance system robustness and decrease rise time, a Modified Model Reference Adaptive System (MMRAS) is implemented to design a new speed controller. Simulation results validate the effectiveness of this proposed method in controlling BLDC motor drives without relying on position sensors while adapting to varying speeds with precision and efficiency. In conclusion, the integration of advanced estimation algorithms and adaptive control strategies showcases promising advancements in optimizing BLDC motor performance under dynamic operating conditions.