Real-time Fault Estimation for a Class of Discrete-Time Linear Parameter-Varying Systems

TL;DR

This paper introduces a real-time fault estimation filter for discrete-time LPV systems that effectively decouples disturbances and parameter variations, validated on automated vehicle dynamics.

Contribution

A novel recursive filter design for fault estimation in LPV systems, formulated as an optimization problem with an approximate scheme for real-time application.

Findings

The filter accurately estimates multiple faults in LPV systems.

It can distinguish faults from disturbances and parameter changes.

Validated on automated vehicle lateral dynamics.

Abstract

Estimating and detecting faults is crucial in ensuring safe and efficient automated systems. In the presence of disturbances, noise or varying system dynamics, such estimation is even more challenging. To address this challenge, this article proposes a novel filter to estimate multiple fault signals for a class of discrete-time linear parameter-varying (LPV) systems. The design of such a filter is formulated as an optimization problem and is solved recursively, while the system dynamics may vary over time. Conditions for existence and detectability of the fault are introduced and the problem is formulated and solved using the quadratic programming framework. We further propose an approximate scheme that can be arbitrarily precise while it enjoys an analytical solution, which supports real-time implementation. The method is illustrated and validated on an automated vehicle's lateral dynamics, which is a practically relevant example for LPV systems. The results show that the estimation filter can decouple unknown disturbances and known or measurable parameter variations in the dynamics while estimating the unknown fault.