Robust adaptive dynamic surface control for hypersonic vehicles

  • Naibao He
  • Qian Gao
  • Hector Gutierrez
  • Changsheng Jiang
  • Yifei Yang
  • Yuchun Bi
Original Paper
  • 27 Downloads

Abstract

An adaptive dynamic surface control (DSC) scheme is proposed for the multi-input multi-output attitude control of near-space hypersonic vehicles (NHV). The proposed control strategy can improve the control performance of NHV despite uncertainties and external disturbances. The proposed controller combines dynamic surface control and radial basis function neural network (RBFNN) and is designed to control the longitudinal dynamics of NHV. The DSC technique is used to handle the problem of “explosion of complexity” inherent to the conventional backstepping method. RBFNN is used to approximate the unknown nonlinear function, and a robustness component is introduced in the controller to cancel the influence of compound disturbance and improve robustness and adaptation of the system. Simulation results show that the proposed strategy possesses good robustness and fast response.

Keywords

Dynamic surface control Near-space hypersonic vehicles Robust control Radial basis function neural network 

List of symbols

\(\alpha \)

The angle of attack (rad)

\(\beta \)

The side slip angle and the roll angle (rad)

\(\gamma \)

The flight path angle (rad)

\({C_\mathrm{D}}\)

Drag coefficient

\({C_\mathrm{L}}\)

Lift coefficient

\({C_\mathrm{T}}\)

Thrust coefficient

D

Drag (lbf)

H

The altitude (ft)

\({I_{yy}}\)

Moment of inertia (slug \(\mathrm{ft^2}\))

L

Lift (lbf)

\({M_{yy}}\)

Pitching moment (lbf ft)

M

Mass (slug)

\(\mu \)

The angle of bank (rad)

q

The pitch rate (rad/s)

\(\bar{r}\)

Radial distance from Earth’s center (ft)

T

Thrust (lbf)

V

The velocity (ft/s)

r

The yaw rate (rad/s)

p

The roll rate (rad/s)

Notes

Acknowledgements

The authors are grateful to the Editor-in-Chief, the Associate Editor, and anonymous reviewers for their constructive comments based on which the presentation of this paper has been greatly improved. This work is partially supported by The Natural Science Foundation of Jiangsu Province (Granted No. BK20150246).

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical and Information EngineeringJiangsu University of TechnologyChangzhouChina
  2. 2.Mechanical and Aerospace EngineeringFlorida Institute of TechnologyMelbourneUSA
  3. 3.School of Computer EngineeringJiangsu University of TechnologyChangzhouChina
  4. 4.College of Automatic EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina

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