Abstract

This paper considers matrix inequality procedures to address the robust fault detection and isolation (FDI) problem for linear time‐invariant systems subject to disturbances, faults, and polytopic or norm‐bounded uncertainties. We propose a design procedure for an FDI filter that aims to minimize a weighted combination of the sensitivity of the residual signal to disturbances and modeling errors, and the deviation of the faults to residual dynamics from a fault to residual reference model, using the ℋ∞‐norm as a measure. A key step in our procedure is the design of an optimal fault reference model. We show that the optimal design requires the solution of a quadratic matrix inequality (QMI) optimization problem. Since the solution of the optimal problem is intractable, we propose a linearization technique to derive a numerically tractable suboptimal design procedure that requires the solution of a linear matrix inequality (LMI) optimization. A jet engine example is employed to demonstrate the effectiveness of the proposed approach.

Highlights

  • In the past decade, great attention has been devoted to the design of model-based fault detection systems and their robustness [1, 2]

  • This paper has addressed the problem of fault detection and isolation for linear time-invariant systems subject to faults, disturbances, and model uncertainties

  • We proposed a performance index that captures the fault detection and isolation (FDI) requirements

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Summary

INTRODUCTION

Great attention has been devoted to the design of model-based fault detection systems and their robustness [1, 2]. Frisk and Nielsen [15] give an algorithm to design a reference model and a robust FDI filter that fits into the framework of standard robust H∞-filtering relying on established and efficient methods Their framework consists in solving two optimization problems successively, which results in a suboptimal solution. Journal of Control Science and Engineering of a QMI optimization is, in general, intractable, we propose a linearization technique to derive a suboptimal design procedure that requires the solution of a numerically tractable.

PROBLEM FORMULATION
C D f Dd Du
MATRIX INEQUALITY FORMULATION
Solution with norm-bounded uncertainties
Solution with polytopic uncertainties
ROBUST FDI FILTER DESIGN USING LMIS
NUMERICAL EXAMPLE
Findings
CONCLUSION
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