Satellite Tracking Control System Using Optimal Variable Coefficients Controllers Based on Evolutionary Optimization Techniques

Year 2023, Volume: 10 Issue: 2, 326 - 348, 31.05.2023

Mohamed El-sayed M. Sakr , Mohamed A. Moustafa Hassan

https://doi.org/10.31202/ecjse.1214722

Cited By: 2

Abstract

Satellite tracking control system is mechanism that redirects the parabolic antenna to the chosen satellite automatically. It perfectly tracks the satellite as it spins across the sky in its orbit. To maintain a continuous communication signal throughout multiple satellite tracking missions, the tracking process must be fast and smooth, with minimal deviations from the target position. Various controller models have been presented over time to address the problem of antenna positioning in satellite systems and to track moveable targets using servomechanism. The purpose of this study is to describe and debate a satellite tracking control system based on a DC servo motor. For optimal tuning of Proportional-Integral-Derivative (PID), Fractional Order PID (FOPID) and Variable Coefficient Fractional Order PID (V-FOPID) controllers that were used in satellite control system, Particle Swarm Optimization (PSO), Gravitational Search Algorithm with Particle Swarm Optimization (GSA-PSO) and Eagle Strategy with Particle Swarm Optimization (ES-PSO) techniques were proposed. Dynamic Performance Indices Based Objective Functions is used to compute the Performance Index. Furthermore, Self-Tuning Fuzzy FOPID (STF-FOPID) is proposed for satellite tracking control system. The system's response is analyzed, and the outcomes of various control strategies are measured and compared to others. The obtained results implies that Variable Coefficient Fractional Order PID controller tuned using Eagle Strategy with Particle Swarm Optimization can precisely trace the desired position with the fastest settling time and free overshoot when compared to other control strategies.

Keywords

Satellite Tracking, Position Control, Nonlinear Controller, Evolutionary Optimization Techniques.

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