NMPC and Deep Learning-Based Vibration Control of Satellite Beam Antenna Dynamics Using PZT Actuators and Sensors

TL;DR

This paper introduces a novel vibration control system for satellite beam antennas using NMPC and Deep Learning with PZT actuators, enhancing stability and response in space environments.

Contribution

It develops a combined NMPC and Deep Learning framework for controlling coupled satellite attitude and beam dynamics with PZT sensors and actuators.

Findings

Effective vibration reduction demonstrated in simulations

Faster response times achieved compared to traditional methods

Enhanced control accuracy and robustness in space conditions

Abstract

This paper presents a novel approach for vibration control of satellite-based flexible beam-type antennas using Nonlinear Model Predictive Control (NMPC) and Deep Learning techniques. The developed control system leverages piezoelectric (PZT) actuators and sensors to manage the coupled attitude and structural dynamics of the satellite, improving precision and stability. We propose a detailed coupled dynamics model that integrates both satellite attitude and beam structural dynamics, considering the effects of PZT-based actuators. Through MATLAB/Simulink simulations, we demonstrate the effectiveness of the combined NMPC and Deep Learning framework in reducing structural vibrations, achieving faster response times, and enhancing overall control accuracy. The results indicate that the proposed system provides a robust solution for controlling flexible beam-type satellite antennas in space environments.