Distributed robust neural network adaptive fault-tolerant control for amorphous flattened air-ground wireless self-assembly network system

With the rapid development of wireless communication technology, how to solve the problem of unknown fault interference (including external unknown interference and internal actuator unknown fault disturbance) encountered during the operation of amorphous flat air-ground wireless self-assembled network systems has become a research hotspot. In this paper, a distributed robust neural network adaptive fault-tolerant controller is designed by incorporating the robust neural network optimal control law into the robust adaptive fault-tolerant controller, and the robust fault-tolerant control factor and adaptive neural network adjustment factor makes the closed-loop wireless self-assembled network system with the active feedback adjustment of the robust fault-tolerant feedback matrix K of the adaptive neural network to make the performance parameters converge to the ideal target value asymptotically, and the system error function can asymptotically converge to zero. The simulation and experimental results show that the system as a whole has good robust fault tolerance performance and active learning performance of the adaptive neural network. Moreover, the stability of the air-ground wireless self-assembly network topology can be relatively improved by 50% when the communication distance between the wireless self-assembly network nodes is 1500 m. This paper provides a certain research basis for the subsequent deployment and application of large-scale long spacing of air-ground wireless self-assembled networks.