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TPC: A Digital Twin-Based Predictive Control Method for Tailplane Control
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DOI：10.1109/TII.2024.3393503.png)
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摘要
En 中文
Tailplane control system (TCS) is a key component to ensure pitch maneuverability and horizontal stability in flight control. However, due to inherent sealing, time-varying, and uncertainty, conventional control methods involve enormous challenges to guarantee optimal operation of the TCS. This article proposes a digital twin-based predictive control method, called twin predictive control (TPC), to explore tailplane optimal control under complex conditions. First, a digital twin of the TCS is established as a predictive model, and twin adaptation is set up to overcome parameter time-varying and uncertainty for supporting accurate prediction. Then, an optimization model is constructed combining tracking error and control adjustment. Based on model transformation, the optimization objective is theoretically proved convex and coupled with the active-set method to enable efficient optimization solving. Finally, the TPC method, integrating the digital twin and the optimization model, is implemented into a physical experimental system to verify the effectiveness of the proposed method. The experimental results and comparisons show that the TPC method can significantly improve tracking and antiinterference performance. Furthermore, it shows merits in excessive adjustment suppression.
Keyword:
Predictive models
Optimization
Real-time systems
Predictive control
Digital twins
Control systems
Uncertainty
Convex optimization
digital twin (DT)
fidelity
predictive control
tailplane control system (TCS)
期刊
IF:
9.9
论文数:
8.6K
被引数:
6.0W
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