arXiv subjects

Tahmin Mahmud

Publications and source records attributed to Tahmin Mahmud.

3 recordsLinked to original sources

arXiv2026

High-Endurance UCAV Propulsion System: A 1-D CNN-Based Real-Time Fault Classification for Tactical-Grade IPMSM Drive

High-performance propulsion for mission-critical applications demands unprecedented reliability and real-time fault resilience. Conventional diagnostic methods (signal-based analysis and standard ML models) are essential for stator/rotor fault detection but suffer from high latency and poor generalization across variable speeds. This paper proposes a 1-D Convolutional Neural Network (CNN) framework for real-time fault classification in the HPDM-350 interior permanent magnet synchronous motor (IPMSM). The proposed architecture extracts discriminative features directly from high-frequency current and speed signals, enabling sub-millisecond inference on embedded controllers. Compared to state-of-the-art long short term memory (LSTM) and classical ML approaches, the 1-D CNN achieves a superior weighted F1-score of 0.9834. Validated through high-fidelity magnetic-domain MATLAB/Simscape models, the method demonstrates robust performance across a +-2700 RPM envelope, providing a lightweight solution for mission-critical electric propulsion systems.

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arXiv2026

Physics-Infused Neural MPC of a DC-DC Boost Converter with Adaptive Transient Recovery and Enhanced Dynamic Stability

DC-DC boost converters require advanced control to ensure efficiency and stability under varying loads. Traditional model predictive control (MPC) and data-driven neural network methods face challenges such as high complexity and limited physical constraint enforcement. This paper proposes a hybrid physics-informed neural network (PINN) combined with finite control set MPC (FCS-MPC) for boost converters. The PINN embeds physical laws into neural training, providing accurate state predictions, while FCS-MPC ensures constraint satisfaction and multi-objective optimization. The method features adaptive transient recovery, explicit duty-ratio control, and enhanced dynamic stability. Experimental results on a commercial boost module demonstrate improved transient response, reduced voltage ripple, and robust operation across conduction modes. The proposed framework offers a computationally efficient, physically consistent solution for real-time control in power electronics.

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arXiv2025

DNN-based Digital Twin Framework of a DC-DC Buck Converter using Spider Monkey Optimization Algorithm

Component ageing is a critical concern in power electronic converter systems (PECSs). It directly impacts the reliability, performance, and operational lifespan of converters used across diverse applications, including electric vehicles (EVs), renewable energy systems (RESs) and industrial automation. Therefore, understanding and monitoring component ageing is crucial for developing robust converters and achieving long-term system reliability. This paper proposes a data-driven digital twin (DT) framework for DC-DC buck converters, integrating deep neural network (DNN) with the spider monkey optimization (SMO) algorithm to monitor and predict component degradation. Utilizing a low-power prototype testbed along with empirical and synthetic datasets, the SMO+DNN approach achieves the global optimum in 95% of trials, requires 33% fewer iterations, and results in 80% fewer parameter constraint violations compared to traditional methods. The DNN model achieves $R^2$ scores above 0.998 for all key degradation parameters and accurately forecasts time to failure ($t_{failure}$). In addition, SMO-tuned degradation profile improves the converter's performance by reducing voltage ripple by 20-25% and inductor current ripple by 15-20%.

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