Design of Fully Automatic Leveling Control System for Vehicle-Mounted Radar Antenna Based on Artificial Intelligence

This paper is dedicated to the design of an AI-based fully automatic leveling control system for car-mounted radar antennas. The car-mounted radar antenna is one of the key electronic devices in automobiles, but its leveling process requires manual intervention, which is inefficient and prone to errors. To solve this problem, this paper constructs an electromechanical radar antenna car seat leveling system structure, establishes a leveling support leg transfer function and a model of the relationship between the leveling platform horizontal angle and the support leg extension length. Then, an AI neural network is applied to implement the automatic leveling control system. By establishing a neural network error compensation prediction model, effective prediction of the leveling support leg extension length and leveling accuracy is achieved. Finally, the software of the leveling control system is designed, and KEIILC51 simulation and debugging are carried out. The system has good adaptability, can meet the full automatic leveling requirements of car-mounted radar antennas in various complex road conditions, and improves leveling efficiency and accuracy. This research is of great significance for the automation control of car-mounted radar antennas.