Fault Estimation Filter Design with Guaranteed Stability Using Markov Parameters
For control engineers, this provides a novel data-driven filter design with guaranteed stability, overcoming limitations of existing methods that require explicit models or lack stabilization capability.
The paper proposes a data-driven method to design a fault estimation filter directly from Markov parameters, enabling stabilization and suboptimal H2 performance without an explicit plant model. Simulations on an unstable aircraft system demonstrate effectiveness.
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.