Particle filter steering engine state prediction method based on improved weight generation mode

Abstract

The invention relates to a particle filter steering engine state prediction method based on an improved weight generation mode. The particle filter steering engine state prediction method based on animproved weight generation mode includes the following steps: 1) F distribution particle filter prediction framework; 2) F kernel construction; and 3) dynamic screening process. The particle filter steering engine state prediction method based on an improved weight generation mode has the advantages that 1) even if the newest current measurement value set does not conform to the normal system development trend and the obtained current particle set at the same time is not in the correct area, the predicted system state can be used for alleviating uncertainty caused by measurement of noise amount through correction of the weight; and 2) compared with a traditional particle filter method, the particle filter steering engine state prediction method based on an improved weight generation mode can obtain better fault prediction accuracy.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis and health management (PHM), in particular to a particle filter-based steering gear state prediction method. Background technique [0002] In the automatic flight control system, the electro-hydraulic combined steering gear is used as the core executive component to drive the three main control surfaces of the elevator, aileron, and rudder, as well as the auxiliary rudder surfaces such as flaps, slats, and trims. As the actuator, the performance and reliability of the steering gear directly affect the performance and reliability of the automatic flight system and even the entire aircraft. With the development of automatic flight technology, the steering gear system has gradually become the focus of current research. [0003] At present, the research on linear dynamic system has formed a relatively mature system both in theory and practical application, and the electro-hydraulic combined s...

AI Technical Summary

Problems solved by technology

For the observation value reconstruction method, the research is mainly focused on the uncertainty propagation of the data-driven prediction method, because the error of the reconstruction measurement value will greatly affect the estimation performance of the particle filter itself, especially when it comes to different properties When the noise term of , it is difficult to form a standard to control the final error of prediction

For a large number of complex systems with time-varying parameters, the component parameters will continue to change before the end of the system's life. For the measurement value reconstruction method that requires a large number of iterative calculations using time-varying models, it is easy to cause cumulative errors

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