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Article

Acceleration Command Tracking via Hierarchical Neural Predictive Control for the Effectiveness of Unknown Control

College of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
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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
(This article belongs to the Section Aeronautics)

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.
Keywords: acceleration command tracking; neural network; model predictive control; dual-layer controller; system state uncertainty acceleration command tracking; neural network; model predictive control; dual-layer controller; system state uncertainty

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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