Since the small initial variation amplitude of soft fault leads to the low detection efficiency of fault detection algorithm based on traditional neural network observer, a soft fault detection algorithm for Flapping Wing Micro Aerial Vehicle (FWMAV) based on multistep neural network observer and adaptive threshold was proposed. Firstly, a multistep prediction observer model was constructed, and the time-delay ability of it can prevent the observer from being polluted by faulty data. Secondly, the window width of the multistep observer was tested and analyzed according to the actual flight data of FWMAV. Thirdly, an adaptive threshold strategy was proposed to perform the fault detection of the observer residuals with the assistance of residual chi-square detection algorithm. Finally, the proposed algorithm was verified and analyzed with the use of actual flight data of FWMAV. Experimental results show that compared with the fault detection algorithm based on traditional neural network observer, the proposed algorithm has the soft fault detection speed increased by 737.5%, and the soft fault detection accuracy increased by 96.1%. It can be seen that the proposed algorithm can effectively improve the soft fault detection speed and accuracy of FWMAV.

There are still some issues existing in the traditional Artificial Potential Field (APF), such as the poor adaptability to the complex environment, easily getting into local standstill and the unsmooth path. In order to solve these problems, an improved artificial potential field method was proposed. Firstly, the connectivity of threats was analyzed by the proposed algorithm, and the optimum feasible solution domain was got by the geometric topology. Secondly, a pre-planning of track points was carried out within the feasible solution domain. The pre-planning was based on the threats' global distribution information, and made up for the deficiencies of falling into local minimum and failing to find a feasible path. Finally, the gravitational function of artificial potential field method was improved, and a sufficient smooth flight path was obtained by several iterations and curvature checking. The simulation results show that the improved algorithm can meet the path planning requirements of unmanned aerial vehicles. The proposed algorithm is simple and feasible with strong searching and adaptability.