Uncertainty Propagation under Residual Disturbances: A Smart-Home Case Study
It addresses uncertainty quantification for control systems under unstructured disturbances, but the validation is limited to a single smart-home case study.
The paper develops a data-driven framework for uncertainty propagation under unmeasured disturbances, validated on smart-home data, achieving efficient uncertainty quantification via polynomial chaos expansions and Chebyshev inequalities.
This paper presents a data-driven framework for uncertainty propagation under unmeasured or statistically unmodeled (unstructured) disturbances. We consider residual disturbances, which consolidate all unstructured disturbances into a single quantity that can be estimated from data. Under mild assumptions, the resulting stochastic predictor is causal and distributionally consistent, enabling efficient uncertainty quantification through polynomial chaos expansions and higher-order Chebyshev inequalities. The proposed method is validated using experimental data from a smart home in Norway.