Neural Network Control of a SOTM Antenna

Abstract

Satcom on the Move (SOTM) antennas are the primary devices for establishing satellite communication in both military and commercial applications. The main design parameters of the SOTM antennas are low cost, low weight, and high data rate. SOTM antennas are basically two or three degrees of freedom robotic manipulators with an antenna payload. In the classical approach, a position and stabilization controller is implemented in order to achieve a high data rate. Most applications use a tracking algorithm to find the maximum RF signal strength by planning a special trajectory for the end effector. In this article, SOTM antennas are modeled and controlled as if they are robotic manipulators. In addition, a neural network controller is implemented to control the robot manipulator and find the maximum RF signal. The neural network controller includes filtered computed torque control (CTM), robustifying signal, and 2 layers neural network structure. The filtered CTM and robustifying signal ensure the closed-loop characteristic, while the neural network structure eliminates nonlinearities and generates the required torque to find the maximum RF signal. The results obtained through a series of simulations demonstrate the desired qualities.