Fault diagnosis method for variable structure fuzzy system sensor and application thereof in flight control system

Abstract

The invention provides a fault diagnosis method for a variable structure fuzzy system sensor and the application thereof in a flight control system. According to the method, an accurate analytical model of a flight control system sensor does not need to be established, and a sensor prediction model is established by means of the global approximation ability of a variable structure fuzzy system, the fault of one or more sensors can be detected, identified and tolerated based on errors between a real model and the prediction model, and the normal work of the flight control system is guaranteed; meanwhile, the variable structure fuzzy system can configure a fuzzy rule base online in real time according to collected sensor data, so that the fault diagnosis method not only has fault tolerance performance, but also has real-time performance and adaptivity.

Description

technical field [0001] The invention relates to a sensor fault diagnosis method of a variable structure fuzzy system and its application in the sensor fault diagnosis of a flight control system. Background technique [0002] The sensor is one of the basic components of the flight control system. It is mainly used to measure the flight status and attitude information of the aircraft. Therefore, the quality of the sensor output signal is related to the performance of the entire system. Any failure of the sensor may lead to the interruption of the source of the feedback signal required by the flight control system, which will adversely affect the performance of the flight control. Therefore, the reliability of the sensor has always been a key issue that needs to be solved in the modern aircraft flight control system. At present, a variety of sensor fault detection and diagnosis methods have been formed, such as hardware redundancy method, analysis redundancy method, signal proc...

AI Technical Summary

Problems solved by technology

The main reason is how to establish a suitable fuzzy rule base. The existing method is to use the experience and knowledge of field operators or relevant experts to establish a fuzzy rule base. Once the fuzzy rule base is established, it is difficult to change it, that is, the self-learning of the system. Poor ability

For complex systems such as airborne sensors, if the fuzzy rules and membership functions are established by using the trial and error method, the design and debugging time will be too long, and the real-time requirements cannot be met.

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