Fault Estimation Filter Design with Guaranteed Stability Using Markov Parameters

TL;DR

This paper introduces a novel data-driven method for designing fault estimation filters directly from Markov parameters, ensuring stability and improved performance without explicit plant models.

Contribution

It proposes a systematic, stability-guaranteed fault filter design approach using Markov parameters, bypassing the need for explicit state-space models.

Findings

Effective fault estimation on an unstable aircraft system

Guarantees stability and suboptimal H2 performance

Flexible filter order selection for performance trade-offs

Abstract

For additive actuator and sensor faults, we propose a systematic method to design a state-space fault estimation filter directly from Markov parameters identified from fault-free data. We address this problem by parameterizing a system-inversion-based fault estimation filter with the identified Markov parameters. Even without building an explicit state-space plant model, our novel approach still allows the filter gain design for stabilization and suboptimal $\mathcal{H}_2$ performance. This design freedom cannot be achieved by other existing data-driven fault estimation filter designs so far. Another benefit of our proposed design is the convenience of determining the state order: a higher state order of the filter leads to better estimation performance, at the cost of heavier computational burden. In contrast, order determination is cumbersome when using an identified state-space plant model for the filter design, because of the complicated propagation of the model mismatch into the fault estimation errors. Simulations using an unstable aircraft system illustrate the effectiveness of the proposed new method.