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

• Published in: IEEE Transactions on Neural Networks and Learning Systems Vol. 33; no. 11; pp. 6226 - 6234
• Main Authors: Liu, Yu, Chen, Xiongbin, Wu, Yilin, Cai, He, Yokoi, Hiroshi
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2022; Institute of Electrical and Electronics Engineers (IEEE); The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive neural network control; Adaptive systems; asymmetric constraint; Asymmetry; Backstepping; backstepping technique; Boundary control; Control theory; Feedback control; Flexible spacecraft; flexible unmanned spacecraft; Liapunov functions; Lyapunov methods; Network control; Neural networks; Nonlinear systems; Nonlinearity; Parameter uncertainty; Robustness (mathematics); Space vehicles; Spacecraft; Stability analysis; Uncertainty; Unmanned spacecraft; Vibration; vibration control; Vibrations
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2021.3072907

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.