Regularized DDPC formulations are convex relaxations of bi-level identification-control problems, and the new A-DDPC algorithm outperforms prior regularized methods by lowering bias and variance errors.
It is thus globally optimal due to the convexity of (A.2) (3) Finally, we demonstrate that all optimal solutions of (A.2) are also optimal solutions of (A.1)
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Regularization in Data-driven Predictive Control: A Convex Relaxation Perspective
Regularized DDPC formulations are convex relaxations of bi-level identification-control problems, and the new A-DDPC algorithm outperforms prior regularized methods by lowering bias and variance errors.