Fault-observer-based iterative learning model predictive controller for trajectory tracking of hypersonic vehicles

doi:10.23919/JSEE.2025.000033

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (3): 803-813.doi: 10.23919/JSEE.2025.000033

• CONTROL THEORY AND APPLICATION • Previous Articles

Fault-observer-based iterative learning model predictive controller for trajectory tracking of hypersonic vehicles

Peng CUI(), Changsheng GAO(), Ruoming AN()

School of Astronautics, Harbin Institute of Technology, Harbin 150001, China

Received:2024-05-09 Accepted:2025-03-02 Online:2025-06-18 Published:2025-07-10
Contact: Changsheng GAO E-mail:pc20211011@163.com;gaocs@hit.edu.cn;anruoming@hit.edu.cn
About author:
CUI Peng was born in 1995. He received his B.S. and M.S. degrees in aircraft design from Harbin Engineering University, Harbin, China, in 2018. He is currently pursuing his Ph.D. degree in Harbin Institute of Technology, Harbin, China. His research interests include hypersonic vehicles and fault-tolerant control. E-mail: pc20211011@163.com

GAO Changsheng was born in 1978. He received his B.S., M.S., and Ph.D. degrees in aircraft design from Harbin Institute of Technology, Harbin, China, in 2002, 2004, and 2007. He is currently a professor in Harbin Institute of Technology, Harbin. His research interests include aircraft intelligent trajectory planning and game confrontation, aircraft situational awareness and trajectory prediction, wide-area flight guidance and control technology. E-mail: gaocs@hit.edu.cn

AN Ruoming was born in 1973. He received his B.S., M.S. and Ph.D. degrees in aircraft design from Harbin Institute of Technology, Harbin, China, in 2000, 2002, and 2006. He is currently an associate professor at the School of Astronautics, Harbin Institute of Technology. His research interests include spacecraft fault diagnosis, aircraft aerodynamic calculation, navigation guidance and fault-tolerant control. E-mail: anruoming@hit.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (12072090).

Abstract

Abstract:

This work proposes the application of an iterative learning model predictive control (ILMPC) approach based on an adaptive fault observer (FOBILMPC) for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles. In order to increase the control amount, this online control legislation makes use of model predictive control (MPC) that is based on the concept of iterative learning control (ILC). By using offline data to decrease the linearized model’s faults, the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed. An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree. During the derivation process, a linearized model of longitudinal dynamics is established. The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.

Key words: hypersonic vehicle, actuator fault, tracking control, iterative learning control (ILC), model predictive control (MPC), fault observer

Peng CUI, Changsheng GAO, Ruoming AN. Fault-observer-based iterative learning model predictive controller for trajectory tracking of hypersonic vehicles[J]. Journal of Systems Engineering and Electronics, 2025, 36(3): 803-813.

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Fault-observer-based iterative learning model predictive controller for trajectory tracking of hypersonic vehicles

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