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.
Then, suppose ( x∗ 1, x∗
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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.