Adaptive Neural Network Control of a Flexible Spacecraft Subject to Input Nonlinearity and Asymmetric Output Constraint

This article focuses on the vibration reducing and angle tracking problems of a flexible unmanned spacecraft system subject to input nonlinearity, asymmetric output constraint, and system parameter uncertainties. Using the backstepping technique, a boundary control scheme is designed to suppress the vibration and regulate the angle of the spacecraft. A modified asymmetric barrier Lyapunov function is utilized to ensure that the output constraint is never transgressed. Considering the system robustness, neural networks are used to handle the system parameter uncertainties and compensate for the effect of input nonlinearity. With the proposed adaptive neural network control law, the stability of the closed-loop system is proved based on the Lyapunov analysis, and numerical simulations are carried out to show the validity of the developed control scheme.

Automated summary

This article focuses on the vibration reducing and angle tracking problems of a flexible unmanned spacecraft system subject to input nonlinearity, asymmetric output constraint, and system parameter uncertainties. A boundary control scheme is designed using the backstepping technique, with a modified asymmetric barrier Lyapunov function to ensure the output constraint is never transgressed. Neural networks are used to handle system parameter uncertainties and compensate for input nonlinearity, proving the stability of the closed-loop system based on Lyapunov analysis.