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null (Ed.)A new learning methodology in terms of a discretization of a so-called Chen-Fliess series of a control affine nonlinear system was recently proposed, in part, for the purpose of systematically including system structure and expert knowledge into control strategies. The main objective of this paper is to appropriately embed this learning unit as a supporting predictive controller for power dynamical systems. In particular, an infinite bus system is used for the prototype design of a smart and active control policy to regulate voltage and frequency. It is demonstrated by simulation how a controller employing a Chen-Fliess learning unit can recover from a fault and address modeling mismatch.more » « less
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null (Ed.)Artificial neural networks have traditionally been used to implement machine learning algorithms. There are, however, alternatives to these biologically inspired machine learning architectures that offer potentially lower complexity and stronger theoretical underpinnings. One such option in the context of control is based on using a generic input-output model known as a Chen-Fliess functional series. The main goal of the paper is to describe a specific architecture that can be used in the multivariable setting to combine both learning and model based control. It builds on recent work by the authors showing that a certain monoid structure underlies any recursive implementation of such a system. The method is demonstrated using a two-input, two-output Lotka-Volterra system.more » « less
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