# A Rapid Fault Reconstruction Strategy Using a Bank of Sliding Mode Observers

## TL;DR

This paper introduces a rapid fault detection method using a bank of sliding mode observers combined with recursive least squares, improving fault detection speed and accuracy.

## Contribution

It proposes a novel adaptive scheme that merges multiple sliding mode observers for faster fault detection, with proven stability and enhanced estimation accuracy.

## Key findings

- Faster fault detection compared to traditional methods
- Stable system performance verified through Lyapunov analysis
- Effective fault isolation demonstrated via simulations

## Abstract

This paper deals with the design of a model-based rapid fault detection and isolation strategy using sliding mode observers. To address this problem, a new scheme is proposed by adaptively combining the information provided by a bank of observers. In this regard, a new structure for sliding mode observers is considered. Then, the well-known recursive least square algorithm is utilized to merge individual state estimations suitably such that the system fault is detected faster. The required condition for enhancing perfect state estimation is derived, and the stability of the overall system is proven via Lyapunov's direct method. The supremacy of proposed scheme is fully discussed through mathematical analyses as well as simulations.