A New Force Control Method by Combining Traditional PID Control with Radial Basis Function Neural Network for a Spacecraft Low-Gravity Simulation System

Abstract

With the continuous development of the space industry, the demand for low-gravity simulation experiments on the ground for spacecraft is increasing, to overcome the gravity compensation of spacecraft on the ground tests. This paper presents a new low-gravity simulation system based on the suspension method. We used a traditional PID control method with Radial Basis Function (RBF) neural network to solve its constant-tension control problem. The ant colony algorithm was used to find the initial parameters of the neural network in the solution space. A self-adjusting control strategy of PID controller parameters was realized. The results show that the tension control error of the low-gravity simulation system is as small as 0.2%, which fully meets the requirements of the system’s technical indicators. This work provides auspicious theoretical and technical support for developing a low-gravity simulation system.

Keywords

• low-gravity simulation
• constant-tension control
• RBF neural network
• PID adaptive