A goal-oriented safe active learning algorithm embedded in MPC with Bayesian RNNs delivers theoretical guarantees of safety and finite-time exploration termination while achieving near-optimal economic performance in simulations.
Thanks to (12d) and (13d), the control actionsu e 0:h⋆−1|k from problem (12) andu p 0|k from (13), respectively, ensure that ¯θ⊤ k x∈ Y= [y min, ymax]for allx∈ X p k
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Goal-oriented safe active learning for predictive control using Bayesian recurrent neural networks
A goal-oriented safe active learning algorithm embedded in MPC with Bayesian RNNs delivers theoretical guarantees of safety and finite-time exploration termination while achieving near-optimal economic performance in simulations.