TPC: A Digital Twin-Based Predictive Control Method for Tailplane Control

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 arti...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industrial informatics 2024-08, Vol.20 (8), p.10269-10279
Main Authors: Cui, Zhexin, Guan, Ruiqi, Yue, Jiguang, Xia, Qian, Wu, Chenhao
Format: Article
Language:English
Subjects:
Active control
Control methods
Control stability
Control systems
Convex optimization
digital twin (DT)
Digital twins
fidelity
Flight control
Horizontal flight
Horizontal tail surfaces
Optimal control
Optimization
Optimization models
Parameter uncertainty
Pitch (inclination)
Prediction models
Predictive control
Predictive models
Real-time systems
tailplane control system (TCS)
Tracking control
Tracking errors
Uncertainty
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2024.3393503