Gimbal control of inertially stabilized platform for airborne remote sensing system based on adaptive RBFNN feedback model

Abstract

The attitude stabilization precision of inertially stabilized platform (ISP) directly affect the resolution of remote sensing image by stabilizing the airborne remote sensing system and holding the line of sight. Therefore, an adaptive control model based on radial basis function neural network (RBFNN) is designed to improve the attitude stabilization precision of ISP gimbal in this article. The adaptive control law and the RBFNN weight adaptive law are designed based on the Lyapunov stability theorem, and the inputs of state feedback control and disturbance observer are combined into the control input. Both simulation and experimental results indicate that the adaptive control based on RBFNN can improve the attitude stabilization precision of ISP gimbal compared to the state feedback control.