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Based on the rainfall data from the automatic weather stations, the conventional data accessed by MICAPS4 and NCEP reanalysis data, we have conducted a comparative analysis of two local heavy rainstorm events on the eastern foot of Helan Mountain in Ningxia occurred respectively on 21 August 2016 and 22 July 2018 in terms of their precipitation characteristics, circulation background and other conditions, with focus on the diagnostic analysis of the moist potential vortex field in the two events. The results indicate that both events can be divided into two stages. In both cases, the first stage are warm-area precipitation, showing the feature such as small range, short time and high intensity. Comparing with the first stage, their second stage of precipitation present larger range and less rain intensity. The severe precipitation during the two events both occurred in the pseudo-equivalent potential temperature (θse) isoline areas, and both of them are distributed along the main axis of the low-level jet. The period of severe precipitation is consistent with the occurrence time of θse maximum. Rainstorm zones are basically located near the center of negative potential vortex (PV), and the trigger and development of rainstorm is consistent with the movement of the negative PV center, whose strengthening, weakening and moving direction can guide the prediction of local heavy rainstorm. The positive large value areas of barotropic term (MPV1) of moist potential vorticity above 500 hPa troposphere are corresponding with the negative large value areas of MPV1 below 700 hPa, and the vertical superposition of positive and negative MPV1 center area is conducive to the occurrence and development of rainstorm. It is found in the vertical section that there are negative extreme areas of baroclinic term (MPV2) of moist potential vorticity at 600 hPa in the two events. The intensity, duration and variation of negative MPV2 extreme areas in the lower and middle troposphere can be used as indicators for the prediction of local heavy rainstorm.

Friction torque disturbance affects the tracking performance of electromechanical actuator servo system, bringing position and speed tracking errors, and even may leading to instability of the servo system. Aiming at the problem of poor tracking performance of electromechanical actuator servo system under friction torque disturbance, a FOANN friction compensation algorithm is proposed to estimate and compensate the friction torque. Firstly, base on LuGre friction model, a electromechanical actuator model is established, and the unmeasured state variable in the LuGre model is estimated by radial basis function neural network. Secondly, a FOANN controller is designed, and the stability of corresponding closed-loop system is proved by Lyapunov stability theory. Finally, through simulation and experimental platform, the dynamic performance of FOANN is compared with those of traditional PD and MRAC. The simulation and experimental results show that, with the proposed FOANN friction torque compensation algorithm, the tracking errors of both position and velocity of electromechanical actuator servo system are greatly reduced. FOANN algorithm can effectively estimate and compensate friction torque, reduce the impact of friction disturbance and enhance the dynamic performance of the servo system.