Gaussian behaviors and stochastic data-driven control

We propose a stochastic behavioral modeling framework, termed Gaussian behaviors, which augments a deterministic linear time-invariant (LTI) behavior with a Gaussian noise component.

We show that this notion is a tractable subclass of stochastic behaviors and encompasses classical parametric stochastic LTI state-space system models as special cases.

Analogously to deterministic LTI behaviors, the framework enables simple and tractable stochastic data-driven control methods.

To this end, we obtain a method for prediction by conditioning the Gaussian behavior on the known part of the trajectory, which is identified directly from the sample covariance of trajectory data.

Building on this method, we develop predictive control formulations that optimize over feedforward or disturbance affine feedback policies.

The resulting formulations are shown to be convex.

We further derive a finite-sample confidence bound on the prediction accounting for both aleatoric and epistemic uncertainty, and incorporate it into a robust control method, for which a tractable convex upper bound is obtained.

Within this framework, subspace predictive control is recovered when only the mean prediction is used, while data-enabled predictive control is shown to account for the prediction uncertainty in an optimistic fashion.

Numerical case studies illustrate the benefits of the proposed methods.