Adaptive Set Observers Design for Nonlinear Continuous-Time Systems: Application to Fault Detection and Diagnosis

TL;DR

This paper introduces a novel set adaptive observer design for nonlinear continuous-time systems that efficiently estimates states and parameters, with applications in fault detection and diagnosis, overcoming traditional computational challenges.

Contribution

It proposes a new set adaptive observer approach based on LPV approximation, reducing complexity in joint state and parameter estimation for nonlinear systems.

Findings

Effective fault detection demonstrated in examples

Reduced computational complexity compared to traditional methods

Successful application to nonlinear system scenarios

Abstract

The paper deals with joint state and parameter estimation for nonlinear continuous-time systems. Based on a guaranteed LPV approximation, the set adaptive observers design problem is solved avoiding the exponential complexity obstruction usually met in the set-membership parameter estimation. Potential application to fault diagnosis is considered. The efficacy of the proposed set adaptive observers is demonstrated on several examples.