Faculty Publications

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Publications by NITK Faculty

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Subject: search.filters.subject.Optimal controls

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    Intelligent adaptive observer-based optimal control of overhead transmission line de-icing robot manipulator
    (Robotics Society of Japan ar@rsj.or.jp, 2016) Vijay, M.; Jena, D.
    [No abstract available]
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    Error analysis of Haar wavelet-based Galerkin numerical method with application to various nonlinear optimal control problems
    (Taylor and Francis Ltd., 2024) Madankar, S.R.; Setia, A.; M, M.; Vatsala, A.S.
    First, this paper defines a general nonlinear optimal control problem with state/control constraints and its approximation problem as the Haar wavelet Galerkin optimal control problem (HWGOCP). Then, a Haar wavelet-based Galerkin numerical method has been developed, which converts it to a nonlinear optimization problem. We theoretically prove that a Haar wavelet feasible solution of HWGOCP will exist. We also show that the approximate solutions of HWGOCP are consistent and converge to the optimal solution of the problem. A variety of application problems have been considered, which include optimal control of tumour growth using Chemotherapy drugs, optimal control of infection via the SIS model using treatment, the Brachistochrone problem in mechanics, optimal control of mold using a fungicide, optimal control of pH value of a chemical reaction to determine the quality of a product, etc. © 2024 Informa UK Limited, trading as Taylor & Francis Group.
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    Automatic generation control of is-landed micro-grid using integral reinforcement learning-based adaptive optimal control strategy
    (Springer Science and Business Media Deutschland GmbH, 2025) Muduli, R.; Jena, D.; Moger, T.
    Abstract: Microgrids serve an essential role in the smart grid infrastructure, facilitating the seamless integration of distributed energy resources and supporting the increased adoption of renewable energy sources to satisfy the growing demand for sustainable energy solutions. This paper presents an application of integral reinforcement learning (IRL) algorithm-based adaptive optimal control strategy for automatic generation control of an is-landed micro-grid. This algorithm is a model-free actor-critic method that learns the critic parameters using the recursive least square method. The actor is straightforward and evaluates the action from the critic directly. The robustness of the proposed control technique is investigated under various uncertainties arising from parameter uncertainty, electric vehicle (EV) aggregator, and renewable energy sources. This study incorporates case studies and comparative analyses to demonstrate the control performance of the proposed control strategy. The effectiveness of the technique is evaluated by comparing it with deep Q-learning (DQN) control techniques and PI controllers. The proposed controller significantly improves performance metrics compared to the DQN and PI controllers. It reduces the peak frequency deviation by 6% and 14%, respectively, compared to the DQN and PI controllers. When subjected to multiple-step load disturbances, the proposed controller reduces the mean square error by 28% and 42%, respectively, while lowering both the integral absolute error and the integral time absolute error by 21% and 35% compared to the DQN and PI controllers. Additionally, when operating with renewable energy sources, the proposed controller decreases the standard deviation in the frequency deviation by 17% compared to the DQN controller and 23% compared to the PI controller. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
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    Haar wavelet-based Galerkin method with its feasibility, consistency, and application to unmanned vehicle navigation around moving obstacles
    (Elsevier Ltd, 2025) Madankar, S.R.; Setia, A.; M, M.; Agarwal, R.P.
    In this study, we propose a novel Haar wavelet-based Galerkin method to solve nonlinear optimal control problems with applications to unmanned vehicle navigation. The method addresses the critical challenge of optimizing energy consumption while ensuring safe navigation in dynamic environments with multiple moving obstacles. By leveraging the computational efficiency and scalability of Haar wavelets, combined with the robustness of the Galerkin approach, we demonstrate convergence to the optimal solution under feasibility and consistency conditions. Comprehensive numerical simulations, including diverse and complex obstacle scenarios, validate the method's practicality. Through detailed trajectory, speed, and direction analyses, we highlight the approach's ability to adapt to real-world navigation challenges, making it a promising tool for autonomous system optimization. © 2025 European Control Association