Active Fault Identification and Robust Control for Unknown Bounded Faults via Volume-Based Costs

TL;DR

This paper introduces a set-membership based active fault diagnosis framework for linear systems that accelerates fault detection without relying on predefined fault models, using volume-based costs and robust tube MPC.

Contribution

It presents a novel active fault diagnosis method combining set-membership identification with volume-based costs, enabling faster detection without predefined fault models.

Findings

Faster fault detection compared to passive strategies.

Effective fault identification without predefined fault models.

Robust constraint satisfaction via tube-based MPC.

Abstract

This paper proposes a novel framework for active fault diagnosis and parameter estimation in linear systems operating in closed-loop, subject to unknown but bounded faults. The approach integrates set-membership identification with a cost function designed to accelerate fault identification. Informative excitation is achieved by minimizing the size of the parameter uncertainty set, which is approximated using ellipsoidal outer bounds. Combining this formulation with a scheduling parameter enables a transition back to nominal control as confidence in the model estimates increases. Unlike many existing methods, the proposed approach does not rely on predefined fault models. Instead, it only requires known bounds on parameter deviations and additive disturbances. Robust constraint satisfaction is guaranteed through a tube-based model predictive control scheme. Simulation results demonstrate that the method achieves faster fault detection and identification compared to passive strategies and adaptive ones based on persistent excitation constraints.