Back-EMF estimation based sensorless control of Brushless DC motor

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.…”

“…However, the use of position sensors increases the cost and installation difficulty of the motor, and the susceptibility to electromagnetic interference may deteriorate the signal measurement in the motor and cause failure [4]. In order to avoid the problems caused by the use of position sensors, obtaining the rotor position and speed information from the electrical signal parameter characteristics of the motor during operation has become an important research direction for motor phase identification and control, such as the phase voltage [5], back electromotive force (BEMF) [6], conduction interval detection of the inverter free-wheel diode [7], current change when the stator core is magnetically saturated [8], state observer [9], artificial intelligence algorithm [9] and other methods. However, these methods increase the complexity of the control system and require large storage space to realize the control strategy and parameter calibration.…”

ANN-based position and speed sensorless estimation for BLDC motors

“…Furthermore, the proposed method achieves a slightly better average performance of the speed estimation over conventional methods, but obtains a worse overall performance of the speed estimation over the advanced ones. These results show that a possible refinement of the method can be considered in the speed estimation based on the methods discussed above, such as the BEMF observer that obtains a significant reduction of the error to 5 rpm [49] with moderate complexity to be applied in precision and critical applications. If the BEMF observer method was considered as the basis for improving the proposed method, the motor BEMF signals should be acquired to feed the inputs of the speed estimation ANN.…”

“…Thirdly, the comparison of the speed estimation performance of the proposed method (22 rpm error in the full range) with respect to the conventional methods is analysed taking into account only the related research that provides numerical data of the speed errors. Some of the most relevant conventional methods are the BEMF Zero-crossing detection with a typical implementation and an improvement with a BEMF observer that obtains speed errors of 5 rpm [9,12,49], the SMO that obtains a speed error of 80 rpm with a typical implementation [29], and the adaptive SMO that obtains an error of 30 rpm [29]. These data show that the proposed method provides an average error reduction of 26.7% and with respect to the best conventional method, an error increment of 340%.…”

The Impact of Computational Accuracy on the Quality of Direct Drive Control

“…In the scalar techniques, the speed or torque of the motor is controlled without precise determination of the rotor position. This position is roughly estimated based on the readings from the Hall effect sensors [7,8] or via BEMF [9,10]. On this basis, the controller turns on the bridge transistors in the appropriate order, forcing the rotor to rotate.…”