Abstract
In this work a sensor-less architecture based on the Extended Kalman Filter observer and on feedback linearization control system is proposed. This work proves that also with a sensor-less architecture (where only the measurements of the machine’s electrical dynamic quantities are available) it is possible to assume a previously control solution, proposed by the authors, to reduce the intrinsic problem of the Cogging Torque. Results in term of the trajectory tracking control problem on the direct current component and on the rotor axis position are presented. An analysis on the initial condition of the rotor axis position revels that the architecture is robust in term of variation of start condition of the global system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Caruso M et al (2015) Analysis, characterization and minimization of IPMSMs cogging torque with different rotor structures. In: 2015 International conference on ecological vehicles and renewable energies (EVER)
Hwang Myeong-Hwan, Lee Hae-Sol, Cha Hyun-Rok (2018) Analysis of torque ripple and cogging torque reduction in electric vehicle traction platform applying rotor notched design. Energies 11(11):3053
Zhang Z, Feng J (2008) Sensorless control of salient PMSM with EKF of speed and rotor position. In: IEEE international conference on electrical machines and systems
Termizi MS et al (2017) Sensorless PMSM drives using Extended Kalman Filter (EKF). In: 2017 IEEE conference on energy conversion (CENCON). IEEE
Tian G et al (2018) Rotor position estimation of sensorless PMSM based on Extented Kalman Filter. In: 2018 IEEE international conference on mechatronics, robotics and automation
Lv H, Wei G, Ding Z (2014) UKF—based for sensorless brushless DC motor control. In: IEEE 2014 international conference on mechatronics and control (ICMC)
Tingna S, Na L, Li W (2010) Sensorless control for brushless DC motors using adaptive sliding mode observer. In: 29th Chinese control conference
Zheng C, Li Y (2016) Sensorless speed control for brushless DC motors system using sliding-mode controller and observers. In: 2016 8th International conference on intelligent human-machine systems and cybernetics (IHMSC), vol 1. IEEE
Vinida K, Chacko M (2016) A novel strategy using H infinity theory with optimum weight selection for the robust control of sensorless brushless DC motor. In: 2016 IEEE symposium on sensorless control for electrical drives (SLED). IEEE
Jinquan Z, Deying G (2014) Control method of sensorless brushless DC motor based on neural network. In: IEEE 26th Chinese control and decision conference
Sun X et al (2016) A sensorless method with torque ripple suppression for brushless DC motors. In: 19th International conference on electrical machines and systems (ICEMS). IEEE
Dini P, Saponara S (2019) Cogging torque reduction in brushless motors by a nonlinear control technique. Energies 12(11):2224
Tudorache T et al (2012) Improved mathematical model of PMSM taking into account cogging torque oscillations. Adv Electr Comput Eng 12(3):59–64
Krause P (2017) Introduction to electric power and drive systems. Wiley, New York
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Pierpaolo, D., Saponara, S. (2020). Control System Design for Cogging Torque Reduction Based on Sensor-Less Architecture. In: Saponara, S., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2019. Lecture Notes in Electrical Engineering, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-37277-4_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-37277-4_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37276-7
Online ISBN: 978-3-030-37277-4
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)