SCIENCE CHINA Information Sciences, Volume 63 , Issue 11 : 212203(2020) https://doi.org/10.1007/s11432-019-2682-y

Robust adaptive control of hypersonic flight vehicle with asymmetric AOA constraint

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  • ReceivedJun 2, 2019
  • AcceptedSep 27, 2019
  • PublishedAug 18, 2020



This work was supported by National Natural Science Foundation of China (Grant Nos. 61622308, 61873206, 61933010), Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (Grant Nos. ICT1900312, ICT20037), Stable Supporting Fund of Science and Technology on Underwater Vehicle Technology (Grant No. SXJQR2018WDKT05), and Synergy Innovation Foundation of the University and Enterprise for Graduate Students in Northwestern Polytechnical University (Grant No. XQ201904).





[1] Sigthorsson D O, Jankovsky P, Serrani A. Robust Linear Output Feedback Control of an Airbreathing Hypersonic Vehicle. J Guidance Control Dyn, 2008, 31: 1052-1066 CrossRef Google Scholar

[2] Hughes H, Wu F. H-infinity LPV state feedback control for flexible hypersonic vehicle longitudinal dynamics. In: Proceedings of AIAA Guidance, Navigation, and Control Conference, 2010. 8281. Google Scholar

[3] Xu H J, Mirmirani M D, Ioannou P A. Adaptive Sliding Mode Control Design for a Hypersonic Flight Vehicle. J Guidance Control Dyn, 2004, 27: 829-838 CrossRef Google Scholar

[4] Xu B, Wang X, Shi Z. Robust Adaptive Neural Control of Nonminimum Phase Hypersonic Vehicle Model. IEEE Trans Syst Man Cybern Syst, 2019, : 1-9 CrossRef Google Scholar

[5] Chen M, Ren B B, Wu Q X. Anti-disturbance control of hypersonic flight vehicles with input saturation using disturbance observer. Sci China Inf Sci, 2015, 58: 1-12 CrossRef Google Scholar

[6] Gao D X, Sun Z Q. Fuzzy tracking control design for hypersonic vehicles via T-S model. Sci China Inf Sci, 2011, 54: 521-528 CrossRef Google Scholar

[7] Xu B, Shi Z K. An overview on flight dynamics and control approaches for hypersonic vehicles. Sci China Inf Sci, 2015, 58: 1-19 CrossRef Google Scholar

[8] Butt W A, Yan L, Kendrick A S. Adaptive dynamic surface control of a hypersonic flight vehicle with improved tracking. Asian J Control, 2013, 15: 594-605 CrossRef Google Scholar

[9] Chen M, Shao S Y, Jiang B. Adaptive Neural Control of Uncertain Nonlinear Systems Using Disturbance Observer.. IEEE Trans Cybern, 2017, 47: 3110-3123 CrossRef PubMed Google Scholar

[10] Xu B, Shou Y X, Luo J, et al.Neural learning control of strict-feedback systems using disturbance observer. IEEE Trans Neural Netw Learn Syst, 2019, 30: 1296--1307. Google Scholar

[11] Xu B, Huang X Y, Wang D W. Dynamic Surface Control of Constrained Hypersonic Flight Models with Parameter Estimation and Actuator Compensation. Asian J Control, 2014, 16: 162-174 CrossRef Google Scholar

[12] Chen M, Tao G. Adaptive Fault-Tolerant Control of Uncertain Nonlinear Large-Scale Systems With Unknown Dead Zone.. IEEE Trans Cybern, 2016, 46: 1851-1862 CrossRef PubMed Google Scholar

[13] Wang F K, Chen W S, Dai H. Backstepping control of a quadrotor unmanned aerial vehicle based on multi-rate sampling. Sci China Inf Sci, 2019, 62: 19203 CrossRef Google Scholar

[14] Pukdeboon C. Extended state observer-based third-order sliding mode finite-time attitude tracking controller for rigid spacecraft. Sci China Inf Sci, 2019, 62: 012206 CrossRef Google Scholar

[15] Xu B. Composite learning finite-time control with application to quadrotors. IEEE Trans Syst Man Cyber Syst, 2018, 48: 1806--1815. Google Scholar

[16] Duan H B, Huo M Z, Yang Z Y. Predator-Prey Pigeon-Inspired Optimization for UAV ALS Longitudinal Parameters Tuning. IEEE Trans Aerosp Electron Syst, 2019, 55: 2347-2358 CrossRef Google Scholar

[17] Qian W, Xing W W, Wang L, et al. New optimal analysis method to stability and $H^{\infty}$ performance of varying delayed systems. ISA Trans, 2019, 93: 137--144. Google Scholar

[18] Qin H D, Yu X, Zhu Z B, et al. An expectation-maximization based single-beacon underwater navigation method with unknown ESV. Neurocomputing, 2020, 378: 295--303. Google Scholar

[19] Lyu X J, Di L, Lin Z L. Characteristic model based all-coefficient adaptive control of an AMB suspended energy storage flywheel test rig. Sci China Inf Sci, 2018, 61: 112204 CrossRef Google Scholar

[20] Duan H B, Yang Q, Deng Y M. Unmanned aerial systems coordinate target allocation based on wolf behaviors. Sci China Inf Sci, 2019, 62: 014201 CrossRef Google Scholar

[21] Zhu W, Zhou Q H, Wang D D. Fully distributed consensus of second-order multi-agent systems using adaptive event-based control. Sci China Inf Sci, 2018, 61: 129201 CrossRef Google Scholar

[22] Rodriguez A, Dickeson J, Cifdaloz O, et al. Modeling and control of scramjet-powered hypersonic vehicles: challenges, trends, and tradeoffs. In: Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit, 2008. 6793. Google Scholar

[23] Smart M K, Hass N E, Paull A. Flight Data Analysis of the HyShot 2 Scramjet Flight Experiment. AIAA J, 2006, 44: 2366-2375 CrossRef Google Scholar

[24] Yao Z H. Control strategy design for scramjet engine with flight/propulsion coupling properties (in Chinese). Dissertation for Ph.D. Degreee. Harbin: Harbin Institute of Technology, 2010. Google Scholar

[25] Liu K L. Research on aerodynamic characteristics of hypersonic inlets with dynamic/steady angle-of-attack (in Chinese). Dissertation for Ph.D. Degreee. Nanjing: Nanjing University of Aeronautics and Astronautics, 2011. Google Scholar

[26] Torrez S, Driscoll J, Dalle D, et al. Hypersonic vehicle thrust sensitivity to angle of attack and mach number. In: Proceedings of AIAA Atmospheric Flight Mechanics Conference, 2009. 6152. Google Scholar

[27] Guo Y Y, Xu B, Hu X X. Two controller designs of hypersonic flight vehicle under actuator dynamics and AOA constraint. Aerospace Sci Tech, 2018, 80: 11-19 CrossRef Google Scholar

[28] Bu X W, Xiao Y, Wang K. A prescribed performance control approach guaranteeing small overshoot for air-breathing hypersonic vehicles via neural approximation. Aerospace Sci Tech, 2017, 71: 485-498 CrossRef Google Scholar

[29] Tee K P, Ge S S. Control of nonlinear systems with partial state constraints using a barrier Lyapunov function. Int J Control, 2011, 84: 2008-2023 CrossRef Google Scholar

[30] Liu Y J, Tong S C. Barrier Lyapunov Functions-based adaptive control for a class of nonlinear pure-feedback systems with full state constraints. Automatica, 2016, 64: 70-75 CrossRef Google Scholar

[31] Tee K P, Ge S S, Tay E H. Barrier Lyapunov Functions for the control of output-constrained nonlinear systems. Automatica, 2009, 45: 918-927 CrossRef Google Scholar

[32] Ren B B, Ge S S, Tee K P. Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function.. IEEE Trans Neural Netw, 2010, 21: 1339-1345 CrossRef PubMed Google Scholar

[33] An H, Xia H W, Wang C H. Barrier Lyapunov function-based adaptive control for hypersonic flight vehicles. NOnlinear Dyn, 2017, 88: 1833-1853 CrossRef Google Scholar

[34] Xu B, Shi Z K, Sun F C. Barrier Lyapunov Function Based Learning Control of Hypersonic Flight Vehicle With AOA Constraint and Actuator Faults.. IEEE Trans Cybern, 2019, 49: 1047-1057 CrossRef PubMed Google Scholar

[35] Liu J X, An H, Gao Y B. Adaptive Control of Hypersonic Flight Vehicles With Limited Angle-of-Attack. IEEE/ASME Trans Mechatron, 2018, 23: 883-894 CrossRef Google Scholar

[36] An H, Wu Q Q, Xia H W. Control of a time-varying hypersonic vehicle model subject to inlet un-start condition. J Franklin Institute, 2018, 355: 4164-4197 CrossRef Google Scholar

[37] Liu Y J, Lu S M, Li D J. Adaptive Controller Design-Based ABLF for a Class of Nonlinear Time-Varying State Constraint Systems. IEEE Trans Syst Man Cybern Syst, 2017, 47: 1546-1553 CrossRef Google Scholar

[38] Parker J T, Serrani A, Yurkovich S. Control-Oriented Modeling of an Air-Breathing Hypersonic Vehicle. J Guidance Control Dyn, 2007, 30: 856-869 CrossRef Google Scholar

[39] Levant A. Robust exact differentiation via sliding mode technique. Automatica, 1998, 34: 379-384 CrossRef Google Scholar

[40] Polycarpou M M. Stable adaptive neural control scheme for nonlinear systems. IEEE Trans Automat Contr, 1996, 41: 447-451 CrossRef Google Scholar

  • Figure 1

    Control scheme.

  • Figure 2

    (Color online) System tracking. (a) Altitude tracking; (b) altitude tracking error; (c) velocity tracking; protectłinebreak (d) velocity tracking error.

  • Figure 3

    (Color online) AOA response. (a) AOA; (b) AOA tracking error.

  • Figure 6

    Response of the system states. (a) FPA; (b) pitch rate; (c) FPA tracking error; (d) pitch rate tracking error.

  • Table A1  

    Table A1Nomenclature

    Parameter DescriptionParameterDescription
    $C_D^{\alpha^i}$The $i$th order coefficient of $\alpha$ contribution to drag $q$ Pitch rate
    $C_L^{\alpha^i}$The $i$th order coefficient of $\alpha$ contribution to lift $\bar~q$ Dynamic pressure
    $C_L^{\delta_e}$Coefficient of $\delta_e$ contribution to lift $S$ Reference area
    $C_M^{\delta_e}$Coefficient of $\delta_e$ contribution to pitch moment $T$ Thrust
    $C_M^{\alpha^i}$Coefficient of $\alpha$ contribution to pitch moment $V$ Velocity
    $\bar~c$Mean aerodynamic chord $V_d$ Velocity reference signal
    $D$Drag $z_T$ Thrust to moment coupling coefficient
    $g$Acceleration due to gravity $\alpha$ Angle of attack
    $h$Altitude $\beta_i$ Thrust fit parameters
    $h_r$Altitude reference signal $\gamma$ Flight path angle
    $I_{yy}$ Moment of inertia $\delta_e$ Elevator deflection
    $L$Lift $\delta_i$ Positive coefficients of modification terms
    $M_{yy}$Pitch momentin adaptive laws
    $m$ Mass $\eta_i$ Positive adaption gains
    $p_{ij}$ Positive parameters of differentiators $\Phi$ Fuel equivalence ratio