Enhancing Dynamic Behavior of Single Phase PM Brushless DC Motor Research Proposal Ref. No. SSTCRC016:
Enhancing Dynamic Behavior of Single Phase PM Brushless DC Motor through Deep Neural Network and Mixture of Experts
Abstract:
The ubiquitous presence of brushless DC motors in various applications, particularly home appliances, underscores their importance as efficient and versatile workhorses. In the contemporary competitive landscape, the brushless DC motor stands out for its high power density, simplicity in driving circuitry, and commendable efficiency. This proposal aims to explore and demonstrate the feasibility of implementing a novel controller for single-phase Permanent Magnet (PM) Brushless DC External Rotor (ER) motors, focusing on improving their dynamic behavior.
Objectives:
Investigate the dynamic behavior of single-phase PM BLDC ER motors, considering factors such as cogging torque and electromotive force (EMF) derived from 2D finite element analyses.
Develop a hybrid control system by integrating linear-quadratic regulator and proportional-integral-derivative methods through a Mixture of Experts (MoE).
Evaluate the performance of the proposed control system under load disturbance, emphasizing enhanced robustness and efficiency.
Utilize ANSYS and MATLAB environments for finite element analysis and dynamic analysis of single-phase PM BLDC ER motors, respectively.
Validate the proposed approach through the implementation of a low-scale experimental setup.
Significance:
The significance of this research lies in its potential to advance the state-of-the-art in brushless DC motor control systems. By leveraging a hybrid control approach and incorporating deep neural networks, the project aims to enhance the performance and robustness of single-phase PM BLDC ER motors, particularly in the face of load disturbances. The outcomes of this research will have practical implications for various applications, such as home appliances, where the efficiency and reliability of motors are crucial.
Methodology:
Dynamic Modeling: Utilize 2D finite element analyses to characterize the dynamic behavior of the single-phase PM BLDC ER motor, focusing on cogging torque and EMF.
Control System Development: Integrate linear-quadratic regulator and proportional-integral-derivative methods using a Mixture of Experts (MoE) to create a hybrid control system.
Performance Evaluation: Assess the proposed control system's performance under load disturbances through simulations in MATLAB, emphasizing improvements in robustness and efficiency.
Finite Element and Dynamic Analysis: Employ ANSYS for finite element analysis and MATLAB for dynamic analysis to validate and refine the theoretical models developed.
Experimental Validation: Implement a low-scale experimental setup to validate the proposed approach in real-world conditions.
Call for Collaborators:
We invite researchers with expertise in the following areas to join this collaborative research initiative:
Control systems for electric motors
Finite element analysis using ANSYS
Dynamic analysis using MATLAB
Deep neural network applications in motor control
Expected Outcome:
The collaborative efforts will lead to a comprehensive understanding of the dynamic behavior of single-phase PM BLDC ER motors and the effectiveness of the proposed hybrid control system. The research findings will be disseminated through academic publications and presentations, contributing to the broader scientific community's knowledge in the field of electric motor control.
Interested researchers are encouraged to express their interest by 20 October 2021, and further details will be shared during an initial online meeting.
For inquiries and expressions of interest, please contact Abby.Lee@scico-stc.com
Thank you for considering collaboration on this exciting and impactful research endeavor. We look forward to the contributions of passionate researchers in advancing the field of electric motor control.