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Learning against uncertainty in control engineering
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DOI：10.1016/j.arcontrol.2022.03.007.png)
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Abstract
En 中文
n this paper, some data-based control design options that can be used to accommodate for the presenceof uncertainties in continuous-state engineering systems are recalled and discussed. Focus is made onreinforcement 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-dominatedperiod for which some suggestions and risks are highlighted
Keywords:
Reinforcement learning
Stochastic model predictive control
Probabilistic certification
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10.7
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836
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ShareSaveShareSaveShareSaveShareSaveShareSaveRandomized methods for design of uncertain systems: Sample complexity and sequential algorithms.png)
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