Learning‐based collision avoidance and robust H∞ optimal formation control for uncertain quadrotor UAV systems

This paper investigates the robust H∞$$ {H}_{\infty } $$ optimal formation control for the position subsystem of quadrotor unmanned aerial vehicles (UAVs) subject to external disturbances and collision constraints. To prevent collision with both members of the formation and external obstacles, a col...

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Veröffentlicht in:International journal of robust and nonlinear control Jg. 34; H. 4; S. 2365 - 2383
Hauptverfasser: Li, Ning, Wang, Hongbin, Luo, Qianda, Zheng, Wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bognor Regis Wiley Subscription Services, Inc 10.03.2024
Schlagworte:
approximate dynamic programming
Closed loops
Collision avoidance
collision avoidance control
Control systems design
Controllers
Differential games
Dynamic programming
Feedback control
Game theory
H-infinity control
Learning
Neural networks
quadrotor UAVs
Robust control
robust H∞$$ {H}_{\infty } $$ control
Subsystems
Unmanned aerial vehicles
Unmanned helicopters
zero‐sum differential game
ISSN:1049-8923, 1099-1239
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Zusammenfassung:This paper investigates the robust H∞$$ {H}_{\infty } $$ optimal formation control for the position subsystem of quadrotor unmanned aerial vehicles (UAVs) subject to external disturbances and collision constraints. To prevent collision with both members of the formation and external obstacles, a collision avoidance potential function is constructed using relative position and velocity information. The basic bounded control input can ensure the stable flight and collision avoidance of a quadrotor UAV system. Based on the approximate dynamic programming (ADP) framework and two‐player zero‐sum differential game theory, the H∞$$ {H}_{\infty } $$ optimal controller is designed to further enhance the control performance of the system. The optimal value function is approximated by a single layer neural network, which avoids solving complex nonlinear Hamilton‐JacobiIsaac (HJI) equation. The stability of the closed loop system is proved. The effectiveness of the robust H∞$$ {H}_{\infty } $$ optimal formation controller based on learning is validated through MATLAB simulation in two distinct scenarios.
Bibliographie:ObjectType-Article-1
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.7086