A Rough Set Theory and Fuzzy Logic-Based Optimization Algorithm for an Automated UWB Microstrip Antenna IPS Azimuth PID Position Adjustment System

This paper explores the application of feedback and control systems in dynamically adjusting the position of a microstrip antenna to optimize signal reception. Ultra-wideband (UWB) antenna-based indoor positioning systems (IPS) have become a significant application, with optimization techniques such...

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Published in2025 International Conference on Communication, Computing, Networking, and Control in Cyber-Physical Systems (CCNCPS) pp. 19 - 24
Main Authors Galleto, Fredelino A., Africa, Aaron Don M., Algarra, Joshua P., Balagtas, Angelo Christian P., Guanzon, Lawrence T., Santiago, Ezekiel Inigo P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 10.06.2025
Subjects
Antenna theory
Azimuth
control systems
Fuzzy logic
IP networks
IPS
Microstrip
microstrip antenna
Microstrip antennas
Optimization
PID controller
rough set theory and fuzzy logic
Rough sets
Telecommunication computing
Ultra wideband antennas
UWB
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DOI10.1109/CCNCPS66785.2025.11135869

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Summary:This paper explores the application of feedback and control systems in dynamically adjusting the position of a microstrip antenna to optimize signal reception. Ultra-wideband (UWB) antenna-based indoor positioning systems (IPS) have become a significant application, with optimization techniques such as rough set theory and fuzzy logic systems being employed. Leveraging MATLAB's Antenna Toolbox and Simulink, the study simulates a microstrip antenna system considering its generated parameters with a center frequency of 2.4 GHz, as well as incorporating a Proportional-Integral- Derivative (PID) controller to compensate the plant. The system adjusts the antenna's azimuth position based on a reference signal from a phone, aiming to maximize signal strength. The control system is modeled through block diagrams, with simulations demonstrating the impact of varying PID controller properties on signal optimization. This project underscores the importance of integrating control theory with practical engineering solutions to solve real-world problems.
DOI:10.1109/CCNCPS66785.2025.11135869