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Sampled-Data Primal-Dual Gradient Dynamics in Model Predictive Control
In this paper, we propose a discrete-time dynamical controller, incorporating specific modifications to the PDG approach, and present stability conditions relevant to the resulting sampled-data system.
Learning Exactly Linearizable Deep Dynamics Models
Research on control using models based on machine-learning methods has now shifted to the practical engineering stage.
Structured Hammerstein-Wiener Model Learning for Model Predictive Control
This paper aims to improve the reliability of optimal control using models constructed by machine learning methods.
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