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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| Vydané v: | International journal of robust and nonlinear control Ročník 34; číslo 4; s. 2365 - 2383 |
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Bognor Regis
Wiley Subscription Services, Inc
10.03.2024
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| Abstract | 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. |
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| AbstractList | This paper investigates the robust 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 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 optimal formation controller based on learning is validated through MATLAB simulation in two distinct scenarios. 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. |
| Author | Li, Ning Wang, Hongbin Zheng, Wei Luo, Qianda |
| Author_xml | – sequence: 1 givenname: Ning orcidid: 0000-0002-6434-537X surname: Li fullname: Li, Ning email: ln940301@163.com organization: The College of the Electrical Engineering of Yanshan University – sequence: 2 givenname: Hongbin surname: Wang fullname: Wang, Hongbin organization: The College of the Electrical Engineering of Yanshan University – sequence: 3 givenname: Qianda surname: Luo fullname: Luo, Qianda organization: The College of the Electrical Engineering of Yanshan University – sequence: 4 givenname: Wei surname: Zheng fullname: Zheng, Wei organization: The College of the Electrical Engineering of Yanshan University |
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| SubjectTerms | 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 |
| Title | Learning‐based collision avoidance and robust H∞ optimal formation control for uncertain quadrotor UAV systems |
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