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INTERNATIONAL JOURNAL OF RECENT TRENDS IN TECHNOLOGY AND ENGINEERING (IJRTTE)

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Adaptive Control of Induction Motors using Deep Neural Networks

Mulagundla Sridevi
Associate Professor, Department of CSE, CVR College of Engineering, Mangalpalli, Ibrahimpatnam, RR, Telangana, India.
K. Deiwakumari
Assistant Professor, Department of Mathematics, Sona College of Technology, Salem, Tamil Nadu, India.

Keywords: Induction motor, adaptive control, deep neural networks, sensor less control, nonlinear systems, fault-tolerant control, energy efficiency, real-time control, Industry 4.0, intelligent motor drives

Abstract

The control of induction motors (IMs) in dynamic and nonlinear environments remains a critical challenge in modern industrial applications. Traditional control strategies such as PID and vector control often fall short in handling system nonlinearities, parameter variations, and real-time adaptability. This paper proposes a deep neural network (DNN) based adaptive control framework for induction motors that addresses these limitations by enabling end-to-end learning of motor dynamics directly from operational data. The proposed system eliminates the need for physical sensors through accurate sensor less estimation of speed and torque, significantly reducing hardware costs and system complexity. Unlike existing models that focus solely on fault detection or offline modelling, our approach integrates real-time control, fault-tolerant compensation, and energy optimization into a unified architecture. The DNN controller demonstrates superior generalization across various motor configurations and seamlessly adapts to disturbances and load changes without manual tuning. Experimental evaluations show enhanced robustness, energy efficiency, and scalability to edge platforms, making the solution highly suitable for Industry 4.0 environments. This work represents a significant advancement toward intelligent, self adaptive, and efficient induction motor control systems.
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Published
2023-09-17
Pages
1-9
Issue
Vol. 2 No. 3 (2023): IJRTTE - 02 - 03
Section
Articles

References

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