Author Identifier (ORCID)
Seyyed Morteza Ghamari: https://orcid.org/0000-0001-5082-820X
Mehrdad Ghahramani: https://orcid.org/0000-0002-5926-0996
Daryoush Habibi: https://orcid.org/0000-0002-7662-6830
Asma Aziz: https://orcid.org/0000-0003-3538-0536
Abstract
Brushless DC (BLDC) motors are commonly used in electric vehicles (EVs) because of their efficiency, small size and great torque-speed performance. These motors have a few benefits such as low maintenance, increased reliability and power density. Nevertheless, BLDC motors are highly nonlinear and their dynamics are very complicated, in particular, under changing load and supply conditions. The above features require the design of strong and adaptable control methods that can ensure performance over a broad spectrum of disturbances and uncertainties. In order to overcome these issues, this paper uses a Fractional-Order Proportional-Integral-Derivative (FOPID) controller that offers better control precision, better frequency response, and an extra degree of freedom in tuning by using non-integer order terms. Although it has the benefits, there are three primary drawbacks: (i) it is not real-time adaptable, (ii) it is hard to choose appropriate initial gain values, and (iii) it is sensitive to big disturbances and parameter changes. A new control framework is suggested to address these problems. First, a Reinforcement Learning (RL) approach based on Deep Deterministic Policy Gradient (DDPG) is presented to optimize the FOPID gains online so that the controller can adjust itself continuously to the variations in the system. Second, Snake Optimization (SO) algorithm is used in fine-tuning of the FOPID parameters at the initial stages to guarantee stable convergence. Lastly, cascade control structure is adopted, where FOPID controllers are used in the inner (current) and outer (speed) loops. This construction adds robustness to the system as a whole and minimizes the effect of disturbances on the performance. In addition, the cascade design also allows more coordinated and smooth control actions thus reducing stress on the power electronic switches, which reduces switching losses and the overall efficiency of the drive system. The suggested RL-enhanced cascade FOPID controller is verified by Hardware-in-the-Loop (HIL) testing, which shows better performance in the aspects of speed regulation, robustness, and adaptability to realistic conditions of operation in EV applications.
Document Type
Journal Article
Date of Publication
10-1-2025
Volume
18
Issue
19
Publication Title
Energies
Publisher
MDPI
School
School of Engineering
RAS ID
83760
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Ghamari, S. M., Ghahramani, M., Habibi, D., & Aziz, A. (2025). Design of a robust adaptive cascade fractional-order proportional–integral–derivative controller enhanced by reinforcement learning algorithm for speed regulation of brushless DC motor in electric vehicles. Energies, 18(19), 5056. https://doi.org/10.3390/en18195056