Data-Driven Robust Receding Horizon Fault Estimation

TL;DR

This paper introduces a data-driven receding horizon fault estimation method that enhances robustness to stochastic identification errors in unknown linear systems, improving fault detection accuracy.

Contribution

It proposes a novel fault estimator based on predictor Markov parameters that accounts for identification errors and provides systematic tuning methods for improved robustness.

Findings

The method achieves asymptotically unbiased fault estimates under certain conditions.

Robust design effectively mitigates the impact of stochastic identification errors.

Simulation results demonstrate superior performance over recent methods.

Abstract

This paper presents a data-driven receding horizon fault estimation method for additive actuator and sensor faults in unknown linear time-invariant systems, with enhanced robustness to stochastic identification errors. State-of-the-art methods construct fault estimators with identified state-space models or Markov parameters, but they do not compensate for identification errors. Motivated by this limitation, we first propose a receding horizon fault estimator parameterized by predictor Markov parameters. This estimator provides (asymptotically) unbiased fault estimates as long as the subsystem from faults to outputs has no unstable transmission zeros. When the identified Markov parameters are used to construct the above fault estimator, zero-mean stochastic identification errors appear as model uncertainty multiplied with unknown fault signals and online system inputs/outputs (I/O). Based on this fault estimation error analysis, we formulate a mixed-norm problem for the offline robust design that regards online I/O data as unknown. An alternative online mixed-norm problem is also proposed that can further reduce estimation errors when the online I/O data have large amplitudes, at the cost of increased computational burden. Based on a geometrical interpretation of the two proposed mixed-norm problems, systematic methods to tune the user-defined parameters therein are given to achieve desired performance trade-offs. Simulation examples illustrate the benefits of our proposed methods compared to recent literature.