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Open AccessArticle
Acceleration Command Tracking via Hierarchical Neural Predictive Control for the Effectiveness of Unknown Control
by
Zhengpeng Yang
Zhengpeng Yang
,
Chao Ming
Chao Ming *
,
Huaiyan Wang
Huaiyan Wang
and
Tongxing Peng
Tongxing Peng
College of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
*
Author to whom correspondence should be addressed.
Aerospace 2025, 12(8), 689; https://doi.org/10.3390/aerospace12080689 (registering DOI)
Submission received: 5 June 2025
/
Revised: 11 July 2025
/
Accepted: 29 July 2025
/
Published: 31 July 2025
Abstract
This paper presents a flight control framework based on neural network Model Predictive Control (NN-MPC) to tackle the challenges of acceleration command tracking for supersonic vehicles (SVs) in complex flight environments, addressing the shortcomings of traditional methods in managing nonlinearity, random disturbances, and real-time performance requirements. Initially, a dynamic model is developed through a comprehensive analysis of the vehicle’s dynamic characteristics, incorporating strong cross-coupling effects and disturbance influences. Subsequently, a predictive mechanism is employed to forecast future states and generate virtual control commands, effectively resolving the issue of sluggish responses under rapidly changing commands. Furthermore, the approximation capability of neural networks is leveraged to optimize the control strategy in real time, ensuring that rudder deflection commands adapt to disturbance variations, thus overcoming the robustness limitations inherent in fixed-parameter control approaches. Within the proposed framework, the ultimate uniform bounded stability of the control system is rigorously established using the Lyapunov method. Simulation results demonstrate that the method exhibits exceptional performance under conditions of system state uncertainty and unknown external disturbances, confirming its effectiveness and reliability.
Share and Cite
MDPI and ACS Style
Yang, Z.; Ming, C.; Wang, H.; Peng, T.
Acceleration Command Tracking via Hierarchical Neural Predictive Control for the Effectiveness of Unknown Control. Aerospace 2025, 12, 689.
https://doi.org/10.3390/aerospace12080689
AMA Style
Yang Z, Ming C, Wang H, Peng T.
Acceleration Command Tracking via Hierarchical Neural Predictive Control for the Effectiveness of Unknown Control. Aerospace. 2025; 12(8):689.
https://doi.org/10.3390/aerospace12080689
Chicago/Turabian Style
Yang, Zhengpeng, Chao Ming, Huaiyan Wang, and Tongxing Peng.
2025. "Acceleration Command Tracking via Hierarchical Neural Predictive Control for the Effectiveness of Unknown Control" Aerospace 12, no. 8: 689.
https://doi.org/10.3390/aerospace12080689
APA Style
Yang, Z., Ming, C., Wang, H., & Peng, T.
(2025). Acceleration Command Tracking via Hierarchical Neural Predictive Control for the Effectiveness of Unknown Control. Aerospace, 12(8), 689.
https://doi.org/10.3390/aerospace12080689
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