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

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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.

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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

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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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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Fang Yang, Ruyi Yuan, Jianqiang Yi, Guoliang Fan & Xiangmin Tan

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  1. Fang Yang
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  2. Ruyi Yuan
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  3. Jianqiang Yi
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  4. Guoliang Fan
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  5. Xiangmin Tan
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Corresponding author

Correspondence to Ruyi Yuan.

Additional information

Communicated by C. Alippi, D. Zaho and D. Liu.

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Yang, F., Yuan, R., Yi, J. et al. Direct adaptive type-2 fuzzy neural network control for a generic hypersonic flight vehicle. Soft Comput 17, 2053–2064 (2013). https://doi.org/10.1007/s00500-013-1123-6

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