Event-Triggered Multi-Model Predictive Control for Hypersonic Flight Vehicles

The large-envelope velocity and altitude tracking of hypersonic flight vehicles is hindered by a combination of dynamic and practical constraints. Dynamically, it suffers from strong nonlinearities and varying operating points; practically, it contends with unavailable states, actuator limits, and the heavy computational burden imposed by periodic predictive optimization. This study proposes a tracking differentiator-based event-triggered state-feedback model predictive control (TD-ETSF-MPC) framework with multiple local prediction models. Three local linear models are built at representative equilibrium points and are activated based on the current operating condition. Meanwhile, a tracking differentiator provides smoothed reference commands, and a Luenberger observer estimates the unmeasured states for feedback. The proposed predictive controller directly accounts for actuator constraints within the finite-horizon framework. To reduce computational cost, online optimization is executed only when the estimated-state error exceeds a preset bound or when the time since the last update reaches the maximum interval. Outside these conditions, the controller keeps the active model and the last computed control command. An event-triggered augmented error model is formulated to explicitly account for model switching, observer estimation errors, input holding, and bounded inter-update intervals. For the nominal closed-loop system, mode-dependent Lyapunov inequalities yield sufficient conditions for local exponential stability; this analysis is further extended to establish uniform ultimate boundedness in the presence of bounded disturbances. Simulation results demonstrate that TD-ETSF-MPC achieves tracking performance comparable to TD-SF-MPC with satisfactory robustness, while its update/hold statistics further confirm that the optimization problem is solved only intermittently, thus significantly reducing redundant computations.

event-triggered control; hypersonic flight vehicles; model predictive control; multi-model control; state observer

1. W. Tang, Z. Yang, C. Jia and H. Gao, “Fixed-time sliding mode controller design for hypersonic vehicles,” Aircraft Engineering and Aerospace Technology, vol. 98, no. 3, pp. 487–501, 2026. [Google Scholar] [Crossref]

2. M. A. Bolender and D. B. Doman, “Nonlinear longitudinal dynamical model of an air-breathing hypersonic vehicle,” Journal of Spacecraft and Rockets, vol. 44, no. 2, pp. 374–387, 2007. [Google Scholar] [Crossref]

3. L. Fiorentini, A. Serrani, M. A. Bolender, and D. B. Doman, “Nonlinear robust adaptive control of flexible air-breathing hypersonic vehicles,” Journal of Guidance, Control, and Dynamics, vol. 32, no. 2, pp. 402–417, 2009. [Google Scholar] [Crossref]

4. Z. Pu, R. Yuan, X. Tan, and J. Yi, “Active robust control of uncertainty and flexibility suppression for air-breathing hypersonic vehicles,” Aerospace Science and Technology, vol. 42, pp. 429–441, 2015. [Google Scholar] [Crossref]

5. D. Q. Mayne, J. B. Rawlings, C. V. Rao, and P. O. M. Scokaert, “Constrained model predictive control: Stability and optimality,” Automatica, vol. 36, no. 6, pp. 789–814, 2000. [Google Scholar] [Crossref]

6. W. Tang, W. Long, and H. Gao, “Model predictive control of hypersonic vehicles accommodating constraints,” IET Control Theory & Applications, vol. 11, no. 15, pp. 2599–2606, 2017. [Google Scholar] [Crossref]

7. X. Tao, N. Li, and S. Li, “Multiple model predictive control for large envelope flight of hypersonic vehicle systems,” Information Sciences, vol. 328, pp. 115–126, 2016. [Google Scholar] [Crossref]

8. D. O. Sigthorsson, P. Jankovsky, A. Serrani, S. Yurkovich, M. A. Bolender, and D. B. Doman, “Robust linear output feedback control of an airbreathing hypersonic vehicle,” Journal of Guidance, Control, and Dynamics, vol. 31, no. 4, pp. 1052–1066, 2008. [Google Scholar] [Crossref]

9. X. Tao, J. Yi, Z. Pu, and T. Xiong, “State-estimator-integrated robust adaptive tracking control for flexible air-breathing hypersonic vehicle with noisy measurements,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 11, pp. 4285–4299, 2019. [Google Scholar] [Crossref]

10. W. P. M. H. Heemels, K. H. Johansson, and P. Tabuada, “An introduction to event-triggered and self-triggered control,” in Proceedings of the 51st IEEE Conference on Decision and Control, Maui, HI, USA, 2012, pp. 3270–3285. [Google Scholar] [Crossref]

11. X. Ge, Q.-L. Han, X.-M. Zhang, and D. Ding, “Dynamic event-triggered control and estimation: A survey,” International Journal of Automation and Computing, vol. 18, no. 6, pp. 857–886, 2021. [Google Scholar] [Crossref]

12. H. Li and Y. Shi, “Event-triggered robust model predictive control of continuous-time nonlinear systems,” Automatica, vol. 50, no. 5, pp. 1507–1513, 2014. [Google Scholar] [Crossref]

13. K. Hashimoto, S. Adachi, and D. V. Dimarogonas, “Event-triggered intermittent sampling for nonlinear model predictive control,” Automatica, vol. 81, pp. 148–155, 2017. [Google Scholar] [Crossref]

14. J. Zhao and M. Chen, “Dynamic event-triggered robust feedback model predictive tracking control of air-breathing hypersonic vehicle based on disturbance preview,” IEEE Transactions on Aerospace and Electronic Systems, vol. 61, no. 2, pp. 3291–3305, 2025. [Google Scholar] [Crossref]

15. J. Han, “From PID to Active Disturbance Rejection Control,” IEEE Transactions on Industrial Electronics, vol. 56, no. 3, pp. 900–906, Mar. 2009. [Google Scholar] [Crossref]

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