Data-driven tube-based stochastic predictive control (2112.04439v2)

Abstract: A powerful result from behavioral systems theory known as the fundamental lemma allows for predictive control akin to Model Predictive Control (MPC) for linear time invariant (LTI) systems with unknown dynamics purely from data. While most of data-driven predictive control literature focuses on robustness with respect to measurement noise, only few works consider exploiting probabilistic information of disturbances for performance-oriented control as in stochastic MPC. In this work, we propose a novel data-driven stochastic predictive control scheme for chance-constrained LTI systems subject to measurement noise and additive stochastic disturbances. In order to render the otherwise stochastic and intractable optimal control problem deterministic, our approach leverages ideas from tube-based MPC by decomposing the state into a deterministic nominal state driven by inputs and a stochastic error state affected by disturbances. By tightening constraints probabilistically with respect to the additive disturbance and robustly with respect to the measurement noise, satisfaction of original chance-constraints is guaranteed. The resulting data-driven receding horizon optimal control problem is lightweight and recursively feasible, and renders the closed loop input-to-state stable with respect to both additive disturbances and measurement noise. We demonstrate the effectiveness of the proposed approach in a simulation example.