Sample-based detectability and moving horizon state estimation of continuous-time systems
This work addresses state estimation challenges in systems with infrequent measurements, such as biomedical applications, but is incremental as it builds on existing detectability and observability concepts.
The paper tackles the problem of state estimation for nonlinear continuous-time systems with irregular output measurements by proposing a sample-based detectability condition and a moving horizon estimation scheme, demonstrating robust stability and applicability in a biomedical simulation.
In this paper we propose a detectability condition for nonlinear continuous-time systems with irregular/infrequent output measurements, namely a sample-based version of incremental integral input/output-to-state stability (i-iIOSS). We provide a sufficient condition for an i-iIOSS system to be sample-based i-iIOSS. This condition is also exploited to analyze the relationship between sample-based i-iIOSS and sample-based observability for linear systems, such that previously established sampling strategies for linear systems can be used to guarantee sample-based i-iIOSS. Furthermore, we present a sample-based moving horizon estimation scheme, for which robust stability can be shown. Finally, we illustrate the applicability of the proposed estimation scheme through a biomedical simulation example.