Distributions and Direct Parametrization for Stable Stochastic State-Space Models
Year: 2025
Type: article
Abstract: We present a direct parametrization for continuous-time stochastic state-space models that ensures external stability via the stochastic bounded-real lemma. Our formulation facilitates the construction of probabilistic priors that enforce almost-sure stability which are suitable for sampling-based Bayesian inference methods. We validate our work with a simulation example and demonstrate its ability to yield stable predictions with uncertainty quantification.
Source: IEEE Control Systems Letters
Authors: Mohamad Al Ahdab, Zheng-Hua Tan, John Leth
Institution: Aalborg University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4410614358