Abstract
This work investigates a neural adaptive appointed-time control for flexible air-breathing hypersonic vehicles subject to modeling nonlinearities, flexible modes, parameter uncertainties and external disturbances. A relative threshold-based neural estimator (RTNE) using minimal learning parameterizations is proposed to online-identify the lumped disturbances with a reduced occupation of communication resource via utilizing intermittent states, while heavy computational burden for online learning is remarkably reduced in the premise of a competitive estimation accuracy. With the estimation results produced by RTNE, a neural adaptive event-triggered control is advanced by incorporating a relative threshold-based sampler into controller-to-actuator channel, such that unnecessary continuous sampling incurring in current time-driven researches can be successfully avoided. Moreover, an appointed-time prescribed performance control is constructed to make the responses of velocity and altitude subsystems evolve within pregiven regions with a user-defined settling time; meanwhile, the strict dependence on the exact knowledge for immeasurable initial system states is removed. The stability of system is proved by virtue of input-to-state stable method, and Zeno behavior is eliminated. Simulations are performed to certify the effectiveness of presented controller.
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Abbreviations
- \(V\) :
-
Velocity
- \(h\) :
-
Altitude
- \(\gamma\) :
-
Flight-path angle (FPA)
- \(\alpha\) :
-
Angle of attack (AoA)
- \(Q\) :
-
Pitch rate (PR)
- \({{\varvec{\upeta}}} \in R^{6 \times 1}\) :
-
Flexible states
- \(f_{i} ,g_{i} ,i = V,\gamma ,\alpha ,Q\) :
-
Modeling nonlinearities
- \(d_{i} ,i = V,\gamma ,\alpha ,Q\) :
-
Overall effects of disturbances
- \(d_{ie} ,i = V,\gamma ,\alpha ,Q\) :
-
External disturbances
- \(\Delta\) :
-
The perturbation of aerodynamic coefficients
- \(\Phi\) :
-
Fuel equivalence ratio (FER)
- \(\delta_{{\text{e}}}\) :
-
Elevator deflection
- \(z_{{\text{T}}}\) :
-
Thrust moment arm
- \(S\) :
-
Reference area
- \(\overline{c}\) :
-
Mean aerodynamics chord
- \(g\) :
-
Gravitation constant
- \(m\) :
-
Vehicle mass
- \(I_{yy}\) :
-
Moment of inertia
- \(T\) :
-
Thrust
- \(D\) :
-
Drag
- \(L\) :
-
Lift
- \(M\) :
-
Pitching moment
- \(N_{i} ,i = 1,2,3\) :
-
Generalized forces
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Acknowledgement
This research has been supported in part by National Natural Science Foundation of China under grant 61803348, National Nature Science Foundation of China as National Major Scientific Instruments Development Project under grant 61927807, State Key Laboratory of Deep Buried Target Damage under grant DXMBJJ2019-02, Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi under grant 2020L0266, Shanxi Province Science Foundation for Youths under grant 201701D221123, Youth AcademicNorth University of China under grant QX201803, Program for the Innovative Talents of Higher Education Institutions of Shanxi, and Shanxi ‘‘1331 Project’’ Key Subjects Construction (1331KSC).
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Shi, Y., Shao, X. Neural adaptive appointed-time control for flexible air-breathing hypersonic vehicles: an event-triggered case. Neural Comput & Applic 33, 9545–9563 (2021). https://doi.org/10.1007/s00521-021-05710-7
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DOI: https://doi.org/10.1007/s00521-021-05710-7