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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Applied Power Engineering (IJAPE)
Behera, Sandip Ranjan
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Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Applied Power Engineering (IJAPE)

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Metaheuristic algorithms for parameter estimation of DC servo motors with quantized sensor measurements Mishra, Debani Prasad; Behera, Sandip Ranjan; Dash, Arul Kumar; Ojha, Prajna Jeet; Salkuti, Surender Reddy
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i1.pp101-108

Abstract

Manufacturing, aviation, and robotics have increased servo motor use due to their precision, reliability, and adaptability in various applications. This study compares three metaheuristic techniques for servo motor model parameter estimation with sensor measurement quantization, focusing on their accuracy and efficiency. Armature resistance, back electromotive force (EMF) constant, torque constant, coil inductance, friction coefficient, and rotor-load inertia are crucial to servo motor behavior prediction, significantly impacting overall system performance. Each approach was rigorously tested and analyzed to evaluate its effectiveness in predicting servo motor characteristics. The results revealed that particle swarm optimization and the firefly algorithm delivered comparable performance, particularly excelling in scenarios where sensor measurement quantization introduced noise or imprecision in the data. These methods demonstrated strong resilience and accuracy under such challenging conditions. In contrast, the genetic algorithm did not perform as well, falling short when compared to the other two techniques in handling noisy or imprecise data, indicating its relative inefficiency in such environments. These findings give servo motor designers and engineers across industries a powerful tool for performance prediction.
Co-Authors Behera, Subhashis Satyabrata Dash, Arul Kumar Mishra, Debani Prasad Ojha, Prajna Jeet Patro, Aditya Ranjan Salkuti, Surender Reddy Sethy, Sai Kanha