2022 10th International Conference on Systems and Control (ICSC) 2022
DOI: 10.1109/icsc57768.2022.9993820
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Control-Oriented Neural State-Space Models for State-Feedback Linearization and Pole Placement

Abstract: Starting from a data set consisting of input-output measurements of a dynamical process, this paper presents a training procedure for a specifically control-oriented model. The considered dynamic model adopts a particular neural statespace representation: its structure guarantees its linearizability by state feedback. Moreover, the linearizing control law follows trivially from the parameters of the learned model. The method relies on a parameterized continuous-time neural state-space model whose structure is … Show more

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