Robust finite time second-order sliding mode stabilization control for floated inertial platform

Abstract

High precision inertial navigation system is one of the main ways to improve the accuracy of the satellite orbit and to prolong life. In order to solve the suspension stabilization problem of the floated inertial navigation platform (or the floated platform), a robust finite time second-order sliding mode control scheme is proposed. This control method can guarantee the stability and rapidity of the system under the complex disturbances. Then, focusing on the design problem of the observer, the nonlinear extended state observer is designed to enhance the adaptability for random uncertainty and to improve the robust performance and the stabilization accuracy of the controller. Finally, the stability and convergence of the control system are proven by the homogeneous theory. The simulation results demonstrate that the proposed method can eliminate the input chattering of the sliding mode control efficiently and the high precision inertial stabilization of the floated inertial platform is realized with the accuracy higher than 0:05.

A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform

“…), which verifies that the original sequence x (0) can be applied to the grey prediction model GM (1,1). Otherwise, it needs to do some necessary transformation processing to make the level ratio within the allowable range.…”

“…Remote Sens. 2021, 13, 4864 6 of 17 (1) GM(1,1) model Construct the drift rate nearest neighbor mean first cumulative sequence (1-AGO) as follows,…”

“…An inertial platform is the core component of a remote sensing system, which has been widely used in military and civil equipment such as aircraft, spacecraft and satellite [1][2][3]. Before service, an inertial platform is stored for a long time.…”

“…Xiaowen Cai [14] applied the empirical mode decomposition (EMD) model to decompose the system part and random part of fiber optic gyroscope (FOG) drift data. The grey model with the ARMA model, ARGM (1,1) model is proposed to predict the partial error of a fiber optic gyroscope (FOG) drift rate system. Chenglong Dai [15] proposed an automatic selection CoG-ARGM prediction model based on cooperative game theory for long-term prediction of the life of hemispherical long time.…”

Adaptive neural network control for stabilizing sphere of floated inertial platform using rotation vector

“…For the stabilization control of the floated inertial platform, Li et al 3 and Yu et al 29 proposed the robust control method based on the extended high gain observer and the robust finite time secondorder sliding mode control method, respectively. However, the sphere rotation is simplified as a single axis system without consideration of the nonuniform mass distribution.…”