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Soft Computing

, Volume 17, Issue 11, pp 2053–2064 | Cite as

Direct adaptive type-2 fuzzy neural network control for a generic hypersonic flight vehicle

  • Fang Yang
  • Ruyi Yuan
  • Jianqiang Yi
  • Guoliang Fan
  • Xiangmin Tan
Focus

Abstract

A direct adaptive interval type-2 fuzzy neural network (IT2-FNN) controller is designed for the first time in hypersonic flight control. The generic hypersonic flight vehicle is a multi-input multi-output system whose longitudinal model is high-order, highly nonlinear, tight coupling and most of all includes big uncertainties. Interval type-2 fuzzy sets with Gaussian membership functions are used in antecedent and consequent parts of fuzzy rules. The IT2-FNN directly outputs elevator deflection and throttle setting which make the GHFV track the altitude command signal and meanwhile maintain its velocity. The parameter adaptive law of IT2-FNN is derived using backpropagation method. The deviation of the control signal from the nominal dynamic inversion control signal is used as the reference output signal of IT2-FNN. The tracking errors of velocity and altitude are used as inputs of IT2-FNN. Tracking differentiator is designed to form an arranged transition process (ATP) of the command signal as well as ATP’s high-order derivatives. Nonlinear state observer is designed to get the approximations of velocity, altitude as well as their high-order derivatives. Simulation results validate the effectiveness and robustness of the proposed controller especially under big uncertainties.

Keywords

Type-2 fuzzy logic Fuzzy neural network Hypersonic Direct adaptive control Uncertainty 

Abbreviations

\(V\)

Velocity, m/s

\(q\)

Pitch rate, rad/s

\(\gamma \)

Flight path angle, rad

\(\alpha \)

Angle of attack, rad

\(h\)

Altitude, m

\(M_y\)

Pitch moment, N m

\(I_y\)

Moment of inertia, \(\mathrm{kg}~\mathrm{m}^2\)

\(r\)

Radial distance from Earth’s center, m

\(\mu \)

Gravitational constant

\(m\)

Mass, kg

\(s\)

Reference area, \(\mathrm{m}^2\)

\(\rho \)

Density of air, \(\mathrm{kg/m}^3\)

\(\bar{c}\)

Mean aerodynamic chord, m

\(R_{\scriptscriptstyle E}\)

Radius of the earth, m

\(\beta \)

Fuel equivalence ratio

\(\delta _t\)

Throttle setting instruction

\(\delta _e\)

Elevator deflection, rad

\(L\)

Lift, N

\(D\)

Drag, N

\(T\)

Thrust, N

\(C_{\scriptscriptstyle L}\)

Lift coefficient

\(C_{\scriptscriptstyle D}\)

Drag coefficient

\(C_{\scriptscriptstyle T}\)

Thrust coefficient

\(C_{\scriptscriptstyle M}\)

Pitch moment coefficient

Notes

Acknowledgments

This work was supported by National Natural Science Foundation of China under Grant 61203003, 61273149 and 60904006, Knowledge Innovation Program of the Chinese Academy of Sciences under Grant YYYJ-1122, and Innovation Method Fund of China under Grant 2012IM010200, and B1320133020.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fang Yang
    • 1
  • Ruyi Yuan
    • 1
  • Jianqiang Yi
    • 1
  • Guoliang Fan
    • 1
  • Xiangmin Tan
    • 1
  1. 1.Institute of AutomationChinese Academy of SciencesBeijingChina

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