In this article, the distributed fault-tolerant tracking control and obstacle avoidance problem is investigated for multiple unmanned aerial vehicles (UAVs) considering lumped disturbances and communication link faults. First, a cerebellar model articulation neural network s introduced to estimate the lumped disturbances. Meanwhile, the distributed virtual leader state observers are used to address unknown communication faults. Then, a distributed nonsingular fast terminal sliding mode formation controller is implemented to track desired trajectory, and a virtual-agent artificial potential function (APF) is designed to accomplish obstacle avoidance. Furthermore, the stability of the closed loop formation control systems with obstacle avoidance is proved using the graph theory and the Lyapunov theory. Finally, simulation results of three fixed-wing UAVs are given to show the effectiveness and good performance of the proposed scheme.
Cerebellar Model Articulation Neural Network-Based Distributed Fault Tolerant Tracking Control With Obstacle Avoidance for Fixed-Wing UAVs
IEEE Transactions on Aerospace and Electronic Systems ; 59 , 5 ; 6841-6852
01.10.2023
3788818 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
End-to-End Learning-Based Obstacle Avoidance for Fixed-Wing UAVs
Springer Verlag | 2023
|DOAJ | 2022
|