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

Seyyed Morteza Ghamari, Mehrdad Ghahramani, Daryoush Habibi and Asma Aziz ()
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Seyyed Morteza Ghamari: School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia
Mehrdad Ghahramani: School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia
Daryoush Habibi: School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia
Asma Aziz: School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia

Energies, 2025, vol. 18, issue 19, 1-27

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.

Keywords: brushless DC motor; FOPID; reinforcement learning; DDPG; Snake Optimization Algorithm; HIL testing; electric vehicles; adaptive control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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