Conditional Generative Modeling of Stochastic LTI Systems: A Behavioral Approach

TL;DR

This paper introduces a data-driven, behavioral conditional generative model for stochastic LTI systems that converges to the true distribution and enhances predictive control without explicit system identification.

Contribution

It proposes a novel behavioral CGM approach that relies solely on trajectory data, with proven convergence and exponential accuracy improvements over traditional methods.

Findings

Convergence of the CGM distribution as data size increases

Exponential decrease in the gap to the true posterior distribution

Effective integration of the CGM into predictive control with verified numerical results

Abstract

This paper presents a data-driven model for Linear Time-Invariant (LTI) stochastic systems by sampling from the conditional probability distribution of future outputs given past input-outputs and future inputs. It operates in a fully behavioral manner, relying solely on the current trajectory and pre-collected input-output data, without requiring explicit identification of system parameters. We refer to this model as a behavioral Conditional Generative Model (CGM). We prove the convergence of the distribution of samples generated by the CGM as the size of the trajectory library increases, with an explicit characterization of the convergence rate. Furthermore, we demonstrate that the gap between the asymptotic distribution of the proposed CGM and the true posterior distribution obtained by Kalman filter, which leverages the knowledge of all system parameters and all historical data, decreases exponentially with respect to the length of past samples. Finally, we integrate this generative model into predictive controllers for stochastic LTI systems. Numerical results verify the derived bounds and demonstrate the effectiveness of the controller equipped with the proposed behavioral CGM.