To close this gap, we develop a framework for designing and analyzing MPC schemes, which are only based on input-output data and come with desirable closed-loop guarantees. We address various control objectives, including setpoint stabilization, tracking, and constraint satisfaction for linear or nonlinear systems and from noise-free or noisy data.
We demonstrate with numerical and experimental applications that the proposed framework not only contributes to a rigorous data-driven control theory, but is also simple to apply and provides high performance for challenging nonlinear control problems.
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