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.
Thus, using Proposi- tion 7, there existsλ∗ w such that the optimal solution sets x∗ 1 for (A.1) and (A.2) are equivalent for all λw > λ ∗ w
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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.