Author Identifier (ORCID)
Seyyed Morteza Ghamari: https://orcid.org/0000-0001-5082-820X
Asma Aziz: https://orcid.org/0000-0003-3538-0536
Daryoush Habibi: https://orcid.org/0000-0002-7662-6830
Abstract
Brushless DC (BLDC) motors are widely used in applications that are highly-efficient, reliable, and compact, such as electric vehicles, robotics, and medical devices. However, the inherent nonlinearities and load sensitivity of BLDC motors require a robust and adaptive control strategy to ensure satisfactory performance under various operating conditions. Sliding mode control (SMC) has been widely used for the BLDC drives. However, because of its simplicity and robustness, the control effectiveness of the control is limited by the sensitivity to the disturbances and the chattering phenomenon. To remedy this, super-twisting (ST) technique has been proposed to achieve smoother response and better robustness to overcome the obvious problem while requiring a large amount of filtering. The incorporation of fractional-order (FO) dynamics adds flexibility, noise resilience and adaptability but FO controllers are still based on offline gain tuning and cannot be used in practice. In order to overcome all these challenges, a novel transfer learning-based fractional-order super-twisting sliding mode controller (TL-FO-ST-SMC) is proposed in this paper. Transfer learning is applied to improve the speed of adaptation on different operating domains by reusing the previous knowledge, thus decreasing the need of repeated offline tuning. Nevertheless, TL itself requires good initial fine-tuning. To this end, antlion optimization (ALO) algorithm is used to find the optimal initial gains which helps in stable learning and robust convergence. A Lyapunov-based stability analysis is given and the proposed method is validated by a typhoon hardware-in-the-loop (HIL) real-time platform. Results show that the TL-FO-ST-SMC has better speed tracking capability, less chattering, disturbance rejection, and faster adaptation than conventional controllers and is suitable for advanced electric vehicle and motor drive applications.
Keywords
adaptive control, brushless DC motors, invertors, robust control
Document Type
Journal Article
Date of Publication
1-1-2026
Volume
20
Issue
1
Publication Title
IET Control Theory & Applications
Publisher
Wiley
School
School of Engineering
RAS ID
95217
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Ghamari, S., Aziz, A., & Habibi, D. (2026). A novel hybrid robust transfer learning-based adaptive fractional-order super-twisting sliding mode controller enhanced for speed regulation of brushless DC motor. IET Control Theory & Applications, 20(1), e70129. https://doi.org/10.1049/cth2.70129