Tube-Based Robust Data-Driven Predictive Control
It provides a theoretically rigorous and computationally tractable data-driven control method for safety-critical systems with bounded noise, though it is an incremental extension of existing tube MPC and data-driven techniques.
This paper proposes a tube-based robust data-driven predictive control scheme for unknown LTI systems using only a single noisy trajectory, achieving recursive feasibility, robust constraint satisfaction, and practical input-to-state stability via a convex QP.
This paper presents a tractable tube-based robust data-driven predictive control scheme that uses only a single finite noisy input-state trajectory of an unknown discrete-time linear time-invariant (LTI) system. A simplex constraint is imposed on the Hankel coefficient vector, yielding explicit polyhedral bounds on the prediction mismatch induced by bounded measurement noise. Using certified initial and terminal robust positively invariant (RPI) sets, we derive a tube-tightened formulation whose online optimization problem is a strictly convex quadratic program (QP). The resulting controller guarantees recursive feasibility, robust satisfaction of input and state constraints, and practical input-to-state stability of the closed loop with respect to measurement noise. Numerical examples illustrate the effectiveness, robustness, and closed-loop performance of the proposed method.