Sensorless Detection of Position and Speed in Brushless DC Motors using the Derivative of Terminal Phase Voltages Technique with a Simple and Versatile Motor Driver Implementation

Abstract

-The detection of position and speed in BLDC motors without using position sensors has meant many efforts for the last decades. The aim of this paper is to develop a sensorless technique for detecting the position and speed of BLDC motors, and to overcome the drawbacks of position sensorbased methods by improving the performance of traditional approaches oriented to motor phase voltage sensing. The position and speed information is obtained by computing the derivative of the terminal phase voltages regarding to a virtual neutral point. For starting-up the motor and implementing the algorithms of the detection technique, a FPGA board with a real-time processor is used. Also, a versatile hardware has been developed for driving BLDC motors through pulse width modulation (PWM) signals. Delta and wye winding motors have been considered for evaluating the performance of the designed hardware and software, and tests with and without load are performed. Experimental results for validating the detection technique were attained in the range 5-1500 rpm and 5-150 rpm under no-load and full-load conditions, respectively. Specifically, speed and position square errors lower than 3 rpm and between 10º-30º were obtained without load. In addition, the speed and position errors after full-load tests were around 1 rpm and between 10º-15º, respectively. These results provide the evidence that the developed technique allows to detect the position and speed of BLDC motors with low accuracy errors at starting-up and over a wide speed range, and reduce the influence of noise in position sensing, which suggest that it can be satisfactorily used as a reliable alternative to position sensors in precision applications.

BLDC Motors Sensorless Control Based on MLP Topology Neural Network

“…During the research process, consider that the testing conditions are similar to those in this experiment as much as possible, and the specific results are shown in Table 8. BEMF ZCD typical 0.5 ~10 [6] BEMF ZCD improvement 0.5 <8 [26] SMO typical 0.18 <80 [27] SMO improvement <0.06 - [28] EKF algorithm <0.03 ~15 [29] Derivative of the terminal phase voltages 0.021~0.025 ~3 [30] Double ANN topology (current and BEMF models) 0.04 -In summary, the method proposed in this article significantly improves the estimation performance of motor rotor position and speed. Timely and accurate rotor information improves the real-time performance of motor control, resulting in the fast dynamic response and high control accuracy of the system, enabling high-performance control of the motor.…”

ANN-based position and speed sensorless estimation for BLDC motors

“…Finally, the comparison of the speed estimation performance of the proposed method with respect to the advanced methods is analysed, and, as indicated previously, only the related research that provide numerical data are taken into account. Some of the most relevant advanced methods are the EKF algorithm that obtains a speed error of 15 rpm [56], the Smoothing Filter algorithm that obtains an error of 10 rpm [56], and the Derivative of the Terminal Phase Voltages that obtains an error between 1 rpm and 3 rpm [38]. These data show that the proposed method provides an average error increment of 144.4% and with respect to the best advanced method, an error increment of 1000%.…”

“…As the operation of the PWM-based three-phase inverter presented high frequency components, they were attenuated with two filters [38]: a restrictive low-pass filter (R a , C a ) for frequencies <20 kHz and a lowpass Pi filter (L b , C b ) for high-frequency spurious components from 100 to 200 kHz. In the final stage of the circuit, a current limiting resistor and clamping Schottky diodes were included to protect the next stage of data processing against overcurrent, undervoltage and overvoltage.…”

“…• The Derivative of the Terminal Phase Voltages [38] obtains a speed error <3 rpm. Its performance is based on the implementation of algorithms hardcoded with specific hardware and embedded software, such as conditioning stages for signal amplification and derivation, and ghost-spike filters for derivation pulses.…”