arXiv subjects

Ajay Pratap Yadav

Publications and source records attributed to Ajay Pratap Yadav.

4 recordsLinked to original sources

arXiv2026

Event-Driven Simulation of Power Electronics Rich Grid Models

Power-electronics systems should be treated according to their natural mathematical structure---inherent switching and discontinuities with piecewise continuous states. Therefore, the simulator should be organized around events, switching topologies, and topology intervals, rather than only around a continuous-time solver that later corrects or smooths discontinuities. This paper presents a simple, event-driven EMT architecture using native C kernels with Python orchestration. With this method, we distill the essential elements of discrete-event simulation applied to power electronics problems and thereby point toward a broad research thrust wherein mature ideas from discrete-event simulation are adapted for use in simulating power electronics circuits.

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arXiv2026

Interoperability of Electric Drive Models using HELICS

Accurate modeling and simulation are essential for the effective design, testing, and evaluation of electric machine systems. However, existing models often face interoperability challenges due to differences in programming languages (e.g., C, MATLAB, Python) and the separation of components such as inverters and controllers across diverse environments. These challenges are amplified by the growing use of advanced simulation platforms like Hardware-in-the-Loop (HIL) and Controller-HIL, which require repeated adaptations for compatibility. This paper presents a HELICS-based co-simulation framework that enables seamless coordination among heterogeneous tools and models, providing a unified platform for integrating and testing electric drive models regardless of their origin. The approach is demonstrated through the co-simulation of an inverter-fed permanent magnet synchronous machine (PMSM) drive under speed control, showcasing reduced development time, flexible reuse of existing models, and efficient integration into both software and HIL environments offering a scalable, modular solution for collaborative and repeatable electric drive system testing and development.

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arXiv2020

Multiobjective Backstepping Controller for Parallel Buck Converter

A backstepping controller is designed for a system of parallel buck converters sharing load. Controller objective is to ensure proper current sharing and output voltage regulation. The designed controller is successfully tested for both constant load and sudden change in loading conditions.

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arXiv2015

Optimal Convergence Rate in Feed Forward Neural Networks using HJB Equation

A control theoretic approach is presented in this paper for both batch and instantaneous updates of weights in feed-forward neural networks. The popular Hamilton-Jacobi-Bellman (HJB) equation has been used to generate an optimal weight update law. The remarkable contribution in this paper is that closed form solutions for both optimal cost and weight update can be achieved for any feed-forward network using HJB equation in a simple yet elegant manner. The proposed approach has been compared with some of the existing best performing learning algorithms. It is found as expected that the proposed approach is faster in convergence in terms of computational time. Some of the benchmark test data such as 8-bit parity, breast cancer and credit approval, as well as 2D Gabor function have been used to validate our claims. The paper also discusses issues related to global optimization. The limitations of popular deterministic weight update laws are critiqued and the possibility of global optimization using HJB formulation is discussed. It is hoped that the proposed algorithm will bring in a lot of interest in researchers working in developing fast learning algorithms and global optimization.

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