Wang, L.; Peng, K.; Zhang, W.; Wang, D. Neural Extended State Observer Based Command Filtered Back-stepping Hypersonic Vehicle Integrated Guidance and Autopilot. Preprints2018, 2018050220. https://doi.org/10.20944/preprints201805.0220.v1
APA Style
Wang, L., Peng, K., Zhang, W., & Wang, D. (2018). Neural Extended State Observer Based Command Filtered Back-stepping Hypersonic Vehicle Integrated Guidance and Autopilot. Preprints. https://doi.org/10.20944/preprints201805.0220.v1
Chicago/Turabian Style
Wang, L., Weihua Zhang and Donghui Wang. 2018 "Neural Extended State Observer Based Command Filtered Back-stepping Hypersonic Vehicle Integrated Guidance and Autopilot" Preprints. https://doi.org/10.20944/preprints201805.0220.v1
Abstract
This paper focuses on the integrated guidance and autopilot design with control input saturation in the end-game phase of hypersonic flight. Firstly, uncertain nonlinear integrated guidance and autopilot model is developed with third actuator dynamics, where the control surface deflection has magnitude constraint. Secondly, neural network is implemented in extended state observer (ESO) design, which is used to estimate the complex model uncertainty, nonlinearity and state coupling. Thirdly, a command filtered back-stepping controller is designed with hybrid sliding surfaces to improve the terminal performance. In the process, different command filters are implemented to avoid the influences of disturbances and repetitive derivation, meanwhile solve the problem of unknown control direction caused by saturation. The stability of closed-loop system is proved by Lyapunov theory, and the principles abided by the controller parameters are concluded through the proof. Finally, series of 6-DOF numerical simulations are presented to show the feasibility and validity of the proposed controller.
Keywords
integrated guidance and autopilot; neural network; extended state observer; command filter; back-stepping control
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
