Data-Driven Predictive Control for Stochastic Descriptor Systems: An Innovation-Based Approach Handling Non-Causal Dynamics
This work addresses control challenges in applications like power networks and chemical processes, offering a novel method for stochastic descriptor systems.
The paper tackles the problem of controlling stochastic descriptor systems with non-causal dynamics by proposing a data-driven predictive control framework that handles algebraic constraints and impulsive modes without explicit system identification, demonstrating effectiveness in numerical experiments on a DC microgrid.
Descriptor systems arise naturally in applications governed by algebraic constraints, such as power networks and chemical processes. The singular system matrix in descriptor systems may introduce non-causal dynamics, where the current output depends on future inputs and, in the presence of stochastic process and measurement noise, on future noise realizations as well. This paper proposes a data-driven predictive control framework for stochastic descriptor systems that accommodates algebraic constraints and impulsive modes without explicit system identification. A causal innovation representation is constructed by augmenting the system state with a noise buffer that encapsulates the non-causal stochastic interactions, transforming the descriptor system into an equivalent proper state-space form. Willems' Fundamental Lemma is then extended to the innovation form with fully data-verifiable conditions. Building on these results, a practical Inno-DeePC algorithm is developed that integrates offline innovation estimation and online predictive control. Numerical experiments on a direct-current (DC) microgrid demonstrate the effectiveness of the proposed approach for stochastic descriptor systems.