Abstract
This paper deals with the control problem of actuator fault and saturation for hypersonic flightvehicles. Different from previous back-stepping design, the scheme is on transforming the dynamics into the“prediction function”. The controller is constructed with high gain observer, while the effect of fault andsaturation is compensated by neural networks. For the input saturation, the auxiliary dynamics is included todesign the adaptive learning law. The neural weights and filtered tracking error are guaranteed to be boundedvia Lyapunov approach. The effectiveness of the proposed method is verified by simulation of winged-conemodel.
Similar content being viewed by others
References
Xu B, Shi Z K. An overview on flight dynamics and control approaches for hypersonic vehicles. Sci China Inf Sci,2015, 58: 070201
Xu H, Mirmirani M D, Ioannou P A. Adaptive sliding mode control design for a hypersonic flight vehicle. J GuidControl Dynam, 2004, 27: 829–838
Gao D X, Sun Z Q. Fuzzy tracking control design for hypersonic vehicles via TS model. Sci China Inf Sci, 2011, 54:521–528
Ataei A, Wang Q. Non-linear control of an uncertain hypersonic aircraft model using robust sum-of-squares method. IET Contr Theory Appl, 2012, 6: 203–215
Gibson T E, Crespo L G, Annaswamy A M. Adaptive control of hypersonic vehicles in the presence of modelinguncertainties. In: Proceedings of American Control Conference, Saint Louis, 2009. 3178–3183.
Chavez F R, Schmidt D K. Uncertainty modeling for multivariable-control robustness analysis of elastic high-speedvehicles. J Guid Control Dynam, 1999, 22: 87–95
Xu B, Sun F, Liu H, et al. Adaptive Kriging controller design for hypersonic flight vehicle via back-stepping. IETContr Theory Appl, 2012, 6: 487–497
Xu B, Wang D, Sun F, et al. Direct neural discrete control of hypersonic flight vehicle. Nonlinear Dyn, 2012, 70:269–278
Fiorentini L, Serrani A. Adaptive restricted trajectory tracking for a non-minimum phase hypersonic vehicle model. Automatica, 2012, 48: 1248–1261
Zhang Z, Hu J. Stability analysis of a hypersonic vehicle controlled by the characteristic model based adaptive controller. Sci China Inf Sci, 2012, 55: 2243–2256
Gao D, Sun Z, Luo X, et al. Fuzzy adaptive control for hypersonic vehicle via backstepping method. Contr TheoryAppl, 2008, 5: 805–810
Chen M, Jiang B. Robust attitude control of near space vehicles with time-varying disturbances. Int J Contr AutomatSyst, 2013, 11: 182–187
Sigthorsson D, Jankovsky P, Serrani A, et al. Robust linear output feedback control of an airbreathing hypersonicvehicle. J Guid Control Dynam, 2008, 31: 1052–1066
Chavez F R, Schmidt D K. Uncertainty modeling for multivariable-control robustness analysis of elastic high-speedvehicles. J Guid Control Dynam, 1999, 22: 87–95
Hu Y N, Yuan Y, Min H B, et al. Multi-objective robust control based on fuzzy singularly perturbed models forhypersonic vehicles. Sci China Inf Sci, 2011, 54: 563–576
Xu B. Robust adaptive neural control of flexible hypersonic flight vehicle with dead-zone input nonlinearity. NonlinearDyn, 2015, 80: 1509–1520
Wang Q, Stengel R F. Robust nonlinear control of a hypersonic aircraft. J Guid Control Dynam, 2000, 23: 577–585
Fiorentini L, Serrani A, Bolender M A, et al. Nonlinear robust adaptive control of flexible air-breathing hypersonicvehicles. J Guid Control Dynam, 2009, 32: 402–417
Gao D X, Sun Z, Du T R. Dynamic surface control for hypersonic aircraft using fuzzy logic system. In: Proceedingsof IEEE International Conference on Automation and Logistics, Jinan,. 2314–2319
Xu B, Huang X, Wang D, et al. Dynamic surface control of constrained hypersonic flight models with parameterestimation and actuator compensation. Asian J Control, 2014, 16: 162–174
Li H, Si Y, Wu L, et al. Guaranteed cost control with poles assignment for a flexible air-breathing hypersonic vehicle. Int J Syst Sci, 2011, 42: 863–876
Wang N, Wu H N, Guo L. Coupling-observer-based nonlinear control for flexible air-breathing hypersonic vehicles. Nonlinear Dyn, 2014, 78: 2141–2159
Farrell J A, Polycarpou M, Sharma M, et al. Command filtered backstepping. IEEE Trans Automat Contr, 2009, 54:1391–1395
Wang D, Huang J. Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systemsin strict-feedback form. IEEE Trans Neural Netw, 2005, 16: 195–202
Xu B, Yang C, Shi Z. Reinforcement learning output feedback NN control using deterministic learning technique. IEEETrans Neural Netw Learn Syst, 2014, 25: 635–641
Zhang T P, Ge S S. Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedbackform. Automatica, 2008, 44: 1895–1903
Xu B, Shi Z, Yang C, et al. Composite neural dynamic surface control of a class of uncertain nonlinear systems instrict-feedback form. IEEE Trans Cybern, 2014, 44: 2626–2634
Hojati M, Gazor S. Hybrid adaptive fuzzy identification and control of nonlinear systems. IEEE Trans Fuzzy Syst,2002, 10: 198–210
Xu B, Shi Z, Yang C. Composite fuzzy control of a class of uncertain nonlinear systems with disturbance observer.Nonlinear Dyn, 2015, 80: 341–351
Park J H, Kim S H, Moon C J. Adaptive neural control for strict-feedback nonlinear systems without backstepping. IEEE Trans Neural Netw, 2009, 20: 1204–1209
Xu B, Gao D X, Wang S X. Adaptive neural control based on HGO for hypersonic flight vehicles. Sci China Inf Sci,2011, 54: 511–520
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, S., Zhang, Y., Jin, Y. et al. Neural control of hypersonic flight dynamics with actuator fault and constraint. Sci. China Inf. Sci. 58, 1–10 (2015). https://doi.org/10.1007/s11432-015-5338-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11432-015-5338-2