Title: Neural network-based robust control for hypersonic flight vehicle with uncertainty modelling

Authors: Yenan Hu, Fuchun Sun, Huaping Liu

Addresses: State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China. ' State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China. ' State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Abstract: Aerodynamic parameters of hypersonic flight vehicle are extremely sensitive to flight condition for its high flight velocity. These usually make the model parameters vary in large range and normal robust control method invalid. This paper evaluates the model uncertainties by traversing the variation values of the flight states. Neural network control is introduced to eliminate the large variations of the model parameters and neural network-based robust controller is designed to realise the tracking control of hypersonic flight vehicle. The simulation results demonstrate the validity of the proposed approach.

Keywords: hypersonic flight vehicles; HFV; model uncertainty; neural networks; tracking control; robust control; uncertainty modelling; aerodynamics; simulation.

DOI: 10.1504/IJMIC.2010.035283

International Journal of Modelling, Identification and Control, 2010 Vol.11 No.1/2, pp.87 - 98

Published online: 20 Sep 2010 *

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