Adaptive control of hypersonic vehicles with unknown dynamics based on dual network architecture

2022 ◽  
Author(s):  
Lin Cheng ◽  
Zhenbo Wang ◽  
Shengping Gong
Keyword(s):  
Adaptive Control ◽  
Network Architecture ◽  
Hypersonic Vehicles

Observer-Based Feedback Adaptive Control for Nonlinear Hypersonic Vehicles

2017 ◽  
Author(s):  
Douglas I. Famularo ◽  
John Valasek ◽  
Jonathan A. Muse ◽  
Michael A. Bolender
Keyword(s):  
Adaptive Control ◽  
Hypersonic Vehicles

Nonlinear Robust Adaptive Control of Flexible Air-Breathing Hypersonic Vehicles

2009 ◽  
Vol 32 (2) ◽  
pp. 402-417 ◽  
Author(s):  
Lisa Fiorentini ◽  
Andrea Serrani ◽  
Michael A. Bolender ◽  
David B. Doman
Keyword(s):  
Adaptive Control ◽  
Robust Adaptive Control ◽  
Hypersonic Vehicles ◽  
Air Breathing ◽  
Robust Adaptive ◽  
Nonlinear Robust

Direct Adaptive Control of Underwater Vehicles using Neural Networks

2003 ◽  
Vol 9 (5) ◽  
pp. 605-619 ◽  
Author(s):  
Myung-Hyun Kim ◽  
Daniel J. Inman
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Network Architecture ◽  
Explicit Knowledge ◽  
Tracking Error ◽  
Network Control ◽  
Lyapunov Theory ◽  
Direct Adaptive Control ◽  
Neural Network Controller ◽  
Comparison Of The Results

A direct adaptive neural network controller is developed for a model of an underwater vehicle. A radial basis neural network and a multilayer neural network are used in the closed-loop to approximate the nonlinear vehicle dynamics. No prior off-line training phase and no explicit knowledge of the structure of the plant are required, and this scheme exploits the advantages of both neural network control and adaptive control. A control law and a stable on-line adaptive law are derived using the Lyapunov theory, and the convergence of the tracking error to zero and the boundedness of signals are guaranteed. A comparison of the results with different neural network architecture is made, and the performance of the controller is demonstrated by computer simulations.

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