Modeling and control of boiler in thermal power plant using model reference adaptive control
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.1.27Keywords:
Boiler, Model Reference Adaptive Control, Modeling, Multi input Multi Output, SimulationDimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The boiler is a multi-variable system, which is very difficult to control due to its nonlinear behavior, uncertainties, interactions between variables, and unmeasured and frequent disturbances. Instead of conventional control techniques, modern control techniques are being implemented in most boilers by industries. Mathematical modeling is a useful tool to analyze a complex system’s performance and design a controller for the same. The mathematical model is derived from the open-loop data obtained from the process station. The mathematical equation is then derived using the decoupling technique in terms of transfer function. An adaptive controller is designed and implemented for the model and the simulation study for the same is carried out using MATLAB. The proposed method discussed in the paper can adjust the controller parameters in response to changes in plant and disturbance in real-time by referring to the reference model that specifies the properties of the desired control system.Abstract
How to Cite
Downloads
Similar Articles
- Divya R., Vanathi P. T., Harikumar R., An optimized cardiac risk levels classifier based on GMM with min- max model from photoplethysmography signals , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- D. Padma Prabha, C. Victoria Priscilla, A combined framework based on LSTM autoencoder and XGBoost with adaptive threshold classification for credit card fraud detection , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Abhishek Dwivedi, Nikhat Raza Khan, Reconfiguration of Automated Manufacturing Systems Using Gated Graph Neural Networks , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- Anita M, Shakila S, Stochastic kernelized discriminant extreme learning machine classifier for big data predictive analytics , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- V. Infine Sinduja, P. Joesph Charles, A hybrid approach using attention bidirectional gated recurrent unit and weight-adaptive sparrow search optimization for cloud load balancing , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- D. Prabakar, Santhosh Kumar D.R., R.S. Kumar, Chitra M., Somasundaram K., S.D.P. Ragavendiran, Narayan K. Vyas, Task offloading and trajectory control techniques in unmanned aerial vehicles with Internet of Things – An exhaustive review , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Nisha Rathore, Purnendu B. Acharjee, K. Thivyabrabha, Umadevi P, Anup Ingle, Davinder kumar, Researching brain-computer interfaces for enhancing communication and control in neurological disorders , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- M. Monika, J. Merline Vinotha, A resilient supply chain model integrating demand variability and carbon emissions in imperfect production systems , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- S. Sindhu, L. Arockiam, A lightweight selective stacking framework for IoT crop recommendation , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Vinodini R, Ritha W, A green inventory model for deteriorating items while producing overtime with nonlinear cost and stock dependent demand , The Scientific Temper: Vol. 16 No. 01 (2025): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
