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| Fault Diagnosis of UAV Formation Actuator Based on Neural Network Observer |
| NIE Rui，WANG Hongru |
| College of Information and Communication Engineering， Harbin Engineering University， Harbin 150000， Heilongjiang， China |
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Abstract With the increase of the number of UAVs, the possibility of fault occurrence and the difficulty of fault diagnosis increase. In this paper, taking the UAV flight control system actuator as the research object, considering the nonlinearity and external disturbance of the flight process, a neural network observer fault diagnosis method for the actuator is proposed, in which the weights and center values of the neural network can be updated online to avoid the difficulty of parameter selection. At the same time, the nonlinear term of the system is effectively dealt with. The relative output error is introduced into the UAV formation by using the long-wingman formation strategy, and the relative output error is described by undirected topological structure diagram, so that the designed neural network observer can reflect the communication connection between UAVs. The Kronecker product is used to represent the global vector of UAV formation system, and the stability conditions of the designed neural network observer are derived from the global point of view by using Lyapunov stability theory. The simulation results show that the designed neural network observer can diagnose the constant and time-varying faults occurring at the same time, different times, same channel and different channels.
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Received: 05 November 2021
Published: 12 July 2022
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2022, Vol. 5 
