Unmanned aerial vehicle longitudinal flight control system fault detection and separation method based on nonlinear adaptive observers

Abstract

The invention relates to an unmanned aerial vehicle longitudinal flight control system fault detection and separation method based on nonlinear adaptive observers. According to the invention, based onan unmanned aerial vehicle longitudinal nonlinear model, a set of nonlinear adaptive observers are designed for actuator and sensor faults of a longitudinal flight control system; a fault separationproblem is transformed into a model matching problem; by referring to the idea of a contribution analysis method, a fault estimation value and the fault direction, which are acquired by each adaptiveobserver, are used to contribute a contribution function, and standardization is carried out; the optimal matching model is determined by selecting the maximum standardized contribution function; andfinally, the maximum standardized contribution function is compared with a threshold value, and fault detection and separation results are acquired. According to the invention, a new reference index is provided for model matching by referring to the contribution analysis method; an analytical model is used to design the nonlinear adaptive observers to acquire a reliable fault estimation value andfault direction; and the problem that the fault direction is difficultly determined in a data driving method is overcome.

Description

technical field [0001] The invention relates to a fault detection and separation method of a longitudinal flight control system of an unmanned aerial vehicle based on a nonlinear adaptive observer, which is used for real-time detection and separation of actuator and sensor faults in the longitudinal flight control system of an unmanned aerial vehicle, and belongs to unmanned aerial vehicles The technical field of system fault diagnosis. Background technique [0002] The research on fault diagnosis technology of complex control system is of great significance to improve the safety and reliability of the system. Taking the UAV flight control system as an example, real-time fault detection and separation provides important technical support for improving the reliability and survivability of the UAV system. At the same time, modern complex control systems usually present the characteristics of strong nonlinearity, multi-source interference, and difficult modeling. However, the...

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Problems solved by technology

However, the disadvantage of data-driven fault isolation methods is that the fault direction is difficult to define precisely

At the same time, because the system is inevitably affected by many factors such as external interference, modeling error, and measurement noise, the traditional model matching method based on the residual evaluation function is in short supply.

On the other hand, in this type of system, once a component fails, its failure mechanism will become very complicated due to the dynamic characteristics of the system and the existence of closed-loop control

Roughly selecting the fault direction using a pure data-driven method will inevitably lead to unreliable fault separation results

[0008] To sum up, the existing fault separation methods cannot effectively solve the problem of fault detection and separation of a class of complex nonlinear control systems such as UAV flight control systems, and this topic still needs further research.

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