Control-Oriented Neural State-Space Models for State-Feedback Linearization and Pole Placement - Archive ouverte HAL Access content directly
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Control-Oriented Neural State-Space Models for State-Feedback Linearization and Pole Placement

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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 state-space 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 inspired from well-known exact linearization. The feasibility and efficiency of the approach is illustrated on a nonlinear identification benchmark, namely the Silverbox one. The quality of learning and linearizing feature of the control design are validated on two nonlinear models by comparing the input-output behavior of each closed-loop and its best linear approximation.
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Dates and versions

hal-03864595 , version 1 (21-11-2022)

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Alexandre Hache, Maxime Thieffry, Mohamed Yagoubi, Philippe Chevrel. Control-Oriented Neural State-Space Models for State-Feedback Linearization and Pole Placement. ICSC'2022: 10th International Conference on Systems and Control, Nov 2022, Marseille, France. ⟨10.1109/ICSC57768.2022.9993820⟩. ⟨hal-03864595⟩
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