Thrust Estimation, Control, and Evaluation for Turbofan Engines

• Published in: IEEE transactions on aerospace and electronic systems Vol. 60; no. 4; pp. 5186 - 5200
• Main Authors: Wen, Si-Xin, Zhang, Yu, Li, Jijun, Wang, Ke, Liu, Kun-Zhi, Sun, Xi-Ming
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computational modeling; Control systems; Controllers; Covariance matrices; Engines; Gain scheduled; Kalman filter; Kalman filters; Noise; Observers; Performance degradation; Performance evaluation; Real time; Real-time systems; Robust control; thrust; Thrust control; turbofan engine; Turbofan engines
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2024.3389937

Given that thrust is the most core parameter of turbofan engines but cannot be measured, and that engines are subject to performance degradation, disturbances, or even failure, this article presents a direct thrust control (DTC) system based on an onboard model and robust control. To overcome the limitations of the unscented Kalman filter in terms of accuracy and convergence speed, the Levenberg–Marquardt algorithm is employed to optimize the measurement update process, thereby establishing a superior onboard model for turbofan engines. Then, to address the real-time issue, the long-short-term memory network is trained to approximate the nonlinear engine model used for the Kalman observer, substantially accelerating the computational efficiency. Finally, based on this onboard model, an H_\infty gain-scheduled controller is designed for the DTC system. The comparative results of numerical simulations and hardware experiments on the Xavier embedded controller substantiate the feasibility, superiority, and real-time performance of our approaches, thus suggesting the potential for practical engineering.