A System Level Approach to Tube-Based Model Predictive Control

Robust tube-based model predictive control (MPC) methods address constraint satisfaction by leveraging an a priori determined tube controller in the prediction to tighten constraints. This letter presents system level tube-MPC (SLTMPC) method derived from parameterization (SLP), which allows optimization over online when solving MPC problem, can significantly reduce conservativeness. We derive SLTMPC establishing equivalence relation between class of robust and SLP. Finally, we show that formulation naturally arises extended SLP its merits numerical example.