Health status assessment of unmanned aerial vehicle (UAV) attitude control system based on an improved multivariate state estimation method

Abstract

In this paper, a health status assessment scheme is studied for the attitude control system (ACS) of fixed-wing unmanned aerial vehicle (FWUAV) based on an improved multivariate state estimation technology that incorporates a dynamic memory matrix. Firstly, the parameters of the FWUAV representing the health status of the ACS are selected as feature parameters, and the historical health data of the feature parameters for the FWUAV is compared with the real-time test data. At the same time, the multivariate state estimation technology is applied to obtain the abnormal degree of components for the ACS. Based on the analytic hierarchy process and the expert experience, the weights are obtained for the different elements at the same functional level of the ACS. Secondly, the functional structure of the ACS is analyzed, and the abnormal degree is calculated for the ACS by combining the concept of reconfigurability and the weight information of each element, and the health status assessment indicators are further established. The health status result for the ACS of the FWUAV is acquired according to the efficiency value of the indicators and the abnormal degree of the ACS. Finally, the effectiveness of the developed algorithm is verified by the simulation analysis.