Fault detection method based on robust interval estimation

Abstract

The invention discloses a fault detection method based on robust interval estimation, and the method comprises the following steps: designing a model uncertainty system considering interference factors, and designing an interval observer for the model uncertainty system; establishing an augmented error system in combination with the model uncertainty system and an interval observer; according to the parameters of the model uncertainty system, optimizing the parameters of the interval observer in combination with the l-infinity criterion, and proposing sufficient conditions for the existence ofthe interval observer; converting a bilinear problem in an interval observer design process into two linear problems by applying an iterative algorithm. The application of the interval observer in the field of fault detection is considered, and the output of the interval observer and the real output of the model uncertainty system are utilized to construct a residual error to detect the fault. The method not only solves the problem of estimation of the system state with uncertain parameters in the system, but also has practical significance and practical value when applied to fault detection.

Description

technical field [0001] The invention relates to the field of system control, in particular to a fault detection method based on robust interval estimation. Background technique [0002] In recent decades, state estimation techniques have received a lot of attention from scholars. Scholars often conduct a lot of research on the theoretical level in combination with control theory and related mathematical tools according to the actual situation. Especially after the appearance of Luenberger observer and Kalman filter, related technologies of state estimation have developed rapidly. However, in engineering applications, due to reasons such as cost-effectiveness or lack of reliability of sensors, the system state value often faces the situation that cannot be measured. This is because in actual situations, there will be some disturbances in the system and output, so some existing methods are difficult to measure accurately. The problems encountered in the practical applicatio...

AI Technical Summary

Problems solved by technology

Usually, since the actual model cannot be oversimplified when transformed into a theoretical model, the complexity of the model is a major obstacle in the design of interval observers

In addition to model complexity, there is another obstacle in the design of observers in the parameter uncertainty interval. Due to the existence of uncertainty, it is almost impossible to design an observer that accurately estimates the state. Although scholars have proposed many methods, but In practice, the state estimation error is almost impossible to zero

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