Abstract
We propose an adaptive learning-based optimal control scheme for height-velocity control models considering model uncertainties and external disturbances of hypersonic winged-cone vehicles. The longitudinal nonlinear model is first established and transformed into the control-oriented error equations, and the control scheme is organized by a steady-compensation combination. To overcome and eliminate the impact of model uncertainties and external disturbances, an adaptive radial basis function neural network (RBFNN) is designed by a q-gradient approach. Taking the height-velocity error system with estimated uncertainties into account, the adaptive learning-based optimal tracking control (ALOTC) scheme is proposed by combining the critic-only adaptive dynamic programming (ADP) framework and parameter optimization of system settling time. Furthermore, a novel weight update law is proposed to satisfy the online iteration requirements, and the algorithm convergence and closed-loop stability are discussed by the Lyapunov theory. Finally, four simulation cases are provided to prove the effectiveness, accuracy, and robustness of the proposed scheme for the hypersonic longitudinal control system.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Nair A P, Selvaganesan N, Lalithambika V R. Lyapunov based PD/PID in model reference adaptive control for satellite launch vehicle systems. Aerosp Sci Tech, 2016, 51: 70–77
An H, Wu Q, Wang G, et al. Adaptive compound control of air-breathing hypersonic vehicles. IEEE Trans Aerosp Electron Syst, 2020, 56: 4519–4532
Yin Z Y, Wang B, Xiong R T, et al. Attitude tracking control of hypersonic vehicle based on an improved prescribed performance dynamic surface control. Aeronaut J, 2024, 128: 875–895
Huang B, Li A, Xu B. Adaptive fault tolerant control for hypersonic vehicle with external disturbance. Int J Adv Robot Syst, 2017, 14: 172988141668713
Guo J, Wang G, Guo Z, et al. New adaptive sliding mode control for a generic hypersonic vehicle. Proc Inst Mech Eng Part G-J Aerosp Eng, 2018, 232: 1295–1303
Guo R, Ding Y, Yue X. Active adaptive continuous nonsingular terminal sliding mode controller for hypersonic vehicle. Aerosp Sci Tech, 2023, 137: 108279
An K, Guo Z, Huang W, et al. Leap trajectory tracking control based on sliding mode theory for hypersonic gliding vehicle. J Zhejiang Univ Sci A, 2022, 23: 188–207
Bellman R. Dynamic programming. Science, 1966, 153: 34–37
Wang N, Gao Y, Zhang X. Data-driven performance-prescribed reinforcement learning control of an unmanned surface vehicle. IEEE Trans Neural Netw Learn Syst, 2021, 32: 5456–5467
Xia R, Bu C, Yan X, et al. Finite-horizon optimal trajectory control of near space hypersonic vehicle with multi-constraints. Optim Control Appl Methods, 2024, 45: 302–320
Hu G, Guo J, Cieslak J, et al. Fault-tolerant control based on adaptive dynamic programming for reentry vehicles subjected to state-dependent actuator fault. Eng Appl Artif Intell, 2023, 123: 106450
Yang H, Hu Q, Dong H, et al. Optimized data-driven prescribed performance attitude control for actuator saturated spacecraft. IEEE ASME Trans Mechatron, 2023, 28: 1616–1626
Wang X, Li Y, Quan Z, et al. Optimal trajectory-tracking guidance for reusable launch vehicle based on adaptive dynamic programming. Eng Appl Artif Intell, 2023, 117: 105497
Lu J, Wei Q, Wang F Y. Parallel control for optimal tracking via adaptive dynamic programming. IEEE CAA J Autom Sin, 2020, 7: 1662–1674
Bao C, Wang P, Tang G. Data-driven based model-free adaptive optimal control method for hypersonic morphing vehicle. IEEE Trans Aerosp Electron Syst, 2022, 59: 3713–3725
Vrabie D, Lewis F. Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems. Neural Networks, 2009, 22: 237–246
Hu G, Guo J, Guo Z, et al. ADP-based intelligent tracking algorithm for reentry vehicles subjected to model and state uncertainties. IEEE Trans Ind Inf, 2023, 19: 6047–6055
He J, Qi R, Jiang B, et al. Adaptive output feedback fault-tolerant control design for hypersonic flight vehicles. J Franklin Inst, 2015, 352: 1811–1835
Sun J, Yi J, Pu Z. Augmented fixed-time observer-based continuous robust control for hypersonic vehicles with measurement noises. IET Control Theor Appl, 2019, 13: 422–433
Guo Y, Xu B. Finite-time deterministic learning command filtered control for hypersonic flight vehicle. IEEE Trans Aerosp Electron Syst, 2022, 58: 4214–4225
Zhao H W, Yang L. Global adaptive neural backstepping control of a flexible hypersonic vehicle with disturbance estimation. Aircr Eng Aerosp Tech, 2022, 94: 492–504
Wang F, Fan P, Fan Y, et al. Robust adaptive control of hypersonic vehicle considering inlet unstart. J Syst Eng Electron, 2022, 33: 188–196
Zhao H, Li R. Typical adaptive neural control for hypersonic vehicle based on higher-order filters. J Syst Eng Electron, 2020, 31: 1031–1040
Kac V, Cheung P. Quantum Calculus. New York: Springer, 2012
Khan S, Naseem I, Malik M A, et al. A fractional gradient descent-based RBF neural network. Circuits Syst Signal Process, 2018, 37: 5311–5332
Hussain S S, Usman M, Siddique T H M, et al. q-RBFNN: A quantum calculus-based RBF neural network, arXiv: 2106.01370
Xu H, Mirmirani M D, Ioannou P A. Adaptive sliding mode control design for a hypersonic flight vehicle. J Guid Control Dyn, 2004, 27: 829–838
Li Y, Qiang S, Zhuang X, et al. Robust and adaptive backstepping control for nonlinear systems using RBF neural networks. IEEE Trans Neural Netw, 2004, 15: 693–701
Liu X, Zhang Y, Wang S, et al. Backstepping attitude control for hypersonic gliding vehicle based on a robust dynamic inversion approach. Proc Inst Mech Eng Part I-J Syst Control Eng, 2014, 228: 543–552
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported by the Natural Science Foundation of Hunan Province (Grant No. 2021JJ10045), the National Natural Science Foundation of China (Grant No. 11972368), and the National Key R&D Program of China (Grant No. 2019YFA0405300).
Rights and permissions
About this article
Cite this article
An, K., Wang, Z. & Huang, W. Adaptive learning-based optimal tracking control system design and analysis of a disturbed nonlinear hypersonic vehicle model. Sci. China Technol. Sci. 67, 1893–1906 (2024). https://doi.org/10.1007/s11431-023-2616-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11431-023-2616-3