A Unified Bayesian Framework for Data-Driven Smoothing, Prediction, and Control
For researchers working on data-driven control and system identification, this provides a systematic method to handle stochastic data without empirical workarounds, though the contribution is incremental as it generalizes existing methods.
This work presents a unified Bayesian framework for linear systems that handles stochastic data-driven smoothing, prediction, and control via maximum a posteriori estimation, generalizing existing algorithms and demonstrating performance improvements over other approaches in numerical examples.
Extending data-driven algorithms based on Willems' fundamental lemma to stochastic data often requires empirical and customized workarounds. This work presents a unified Bayesian framework for linear systems that provides a systematic and general method for handling stochastic data-driven tasks, including smoothing, prediction, and control, via maximum a posteriori estimation. This framework formulates a unified trajectory estimation problem for the three tasks by specifying different types of trajectory knowledge. Then, a Bayesian problem is solved that optimally combines trajectory knowledge with a data-driven characterization of the trajectory from offline data for correlated input-output uncertainties with elliptical distributions. Under specific conditions, this problem is shown to generalize existing data-driven prediction and control algorithms. Numerical examples demonstrate the performance of the unified approach for all three tasks against other data-driven and system identification approaches.