Title
Air-Breathing Hypersonic Vehicle Tracking Control Based on Adaptive Dynamic Programming
Document Type
Article
Date of Original Version
3-1-2017
Abstract
In this paper, we propose a data-driven supplementary control approach with adaptive learning capability for air-breathing hypersonic vehicle tracking control based on action-dependent heuristic dynamic programming (ADHDP). The control action is generated by the combination of sliding mode control (SMC) and the ADHDP controller to track the desired velocity and the desired altitude. In particular, the ADHDP controller observes the differences between the actual velocity/altitude and the desired velocity/altitude, and then provides a supplementary control action accordingly. The ADHDP controller does not rely on the accurate mathematical model function and is data driven. Meanwhile, it is capable to adjust its parameters online over time under various working conditions, which is very suitable for hypersonic vehicle system with parameter uncertainties and disturbances. We verify the adaptive supplementary control approach versus the traditional SMC in the cruising flight, and provide three simulation studies to illustrate the improved performance with the proposed approach.
Publication Title, e.g., Journal
IEEE Transactions on Neural Networks and Learning Systems
Volume
28
Issue
3
Citation/Publisher Attribution
Mu, Chaoxu, Zhen Ni, Changyin Sun, and Haibo He. "Air-Breathing Hypersonic Vehicle Tracking Control Based on Adaptive Dynamic Programming." IEEE Transactions on Neural Networks and Learning Systems 28, 3 (2017): 584-598. doi: 10.1109/TNNLS.2016.2516948.