STM Journals Peer-reviewed research from Consortium e-Learning Network

4 publications

  • Published Subscription Review Article

    LQR-Based Optimal Control of Inverted Pendulum System with State Estimation and Stability Analysis

    By Amit Pandhare, Aditya Kumbhar, Vaibhav Godase

    Abstract: The inverted pendulum on a cart is a canonical benchmark problem in control systems engineering, capturing the essential challenges of stabilizing an inherently unstable, underactuated, and nonlinear plant. Classical Proportional-Integral-Derivative (PID) controllers, while widely employed in industrial practice, exhibit fundamental performance limitations when applied to such systems, primarily due to their inability to account for multivariable coupling, process noise, and the absence of a systematic optimization framework. This paper presents …

    Published in International Journal of Advanced Control and System Engineering · Vol. 4, Issue 1, 2026 · pp. 31–43 Read article

  • Published Subscription Review Article

    AI-Assisted Optimization of Supersonic Airfoil Shapes Using CFD Coupling

    By Amit Pandhare, Aditya Kumbhar, Vaibhav Godase

    Abstract: This paper presents a novel framework for optimizing supersonic airfoil geometries through integrated artificial intelligence and computational fluid dynamics coupling. Traditional gradient-based optimization methods for high-speed aerodynamic shapes suffer from computational expense and convergence difficulties in non-convex design spaces. The proposed methodology employs a deep neural network surrogate model trained on high-fidelity Reynolds-Averaged Navier-Stokes solutions to approximate aerodynamic performance metrics across the design space. A hybrid particle swarm-genetic algorithm searches …

    Published in Journal of Aerospace Engineering & Technology · Vol. 16, Issue 1, 2026 Read article

  • Published Subscription Review Article

    Machine Learning Assisted Optimization of Nanoscale MOSFET Parameters Using TCAD Simulation

    By Amit Pandhare, Aditya Kumbhar, Vaibhav Godase

    Abstract: This paper presents a machine learning (ML) assisted framework for the multi-objective optimization of nanoscale bulk n-channel metal-oxide-semiconductor field-effect transistors (nMOSFETs) with a 10 nm physical gate length, high-k HfO₂ gate dielectric, and TiN metal gate. Technology computer-aided design (TCAD) simulations employing drift-diffusion transport, Shockley-Read-Hall recombination, Lombardi mobility degradation, and density- gradient quantum correction models are used to generate a parametric dataset of 2,400 device configurations spanning gate length (L₝), …

    Published in Journal of Microelectronics and Solid State Devices · Vol. 13, Issue 1, 2026 · pp. 10–19 Read article

  • Published Subscription Review Article

    AI-Assisted Gain Scheduling for Real-Time Temperature Control in Chemical Reactors

    By Amit Pandhare, Aditya Kumbhar, Vaibhav Godase

    Abstract: Temperature control in continuous stirred-tank reactors (CSTR) represents a critical challenge in chemical process industries due to inherent nonlinearities, time-varying dynamics, and parametric uncertainties. Conventional proportional-integral-derivative (PID) controllers with fixed gains often fail to maintain optimal performance across varying operating conditions, leading to temperature excursions that compromise product quality and safety. This paper presents a novel AI-assisted gain scheduling framework that integrates artificial neural networks (ANN) with adaptive PID control …

    Published in Journal of Control & Instrumentation · Vol. 17, Issue 1, 2026 · pp. 24–33 Read article

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