Learning Against Uncertainty in Control Engineering
Résumé
In this paper, some data-based control design options that can be used to accommodate for the presence of uncertainties in continuous-state engineering systems are recalled and discussed. Focus is made on reinforcement learning, stochastic model predictive control and certification via randomized optimization. Some thoughts are also shared regarding the positioning of the control community in a data and AI-dominated period for which some suggestions and risks are highlighted.
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