Fundamental Entropic Laws and L_p Limitations of Feedback Systems: Implications for Machine-Learning-in-the-Loop Control
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In this paper, we study the fundamental performance limitations for generic feedback systems in which both the controller and the plant may be arbitrarily causal while the disturbance can be with any distributions. In particular, we obtain fundamental L_p bounds based on the entropic laws that are inherent in any feedback systems. We also examine the implications of the generic bounds for machine-learning-in-the-loop control; in other words, fundamental limits in general exist to what machine learning elements in feedback loops can achieve.
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