Data-driven control and transfer learning using neural canonical control structures

Description

An indirect data-driven control and transfer learning approach based on a data-driven feedback linearization with neural canonical control structures is proposed. An artificial neural network auto-encoder structure trained on recorded sensor data is used to approximate state and input transformations for the identification of the sampled-data system in Brunovsky canonical form. The identified transformations, together with a designed trajectory controller, can be transferred to a system with varied parameters, where the neural network weights are adapted using newly collected recordings. The proposed approach is demonstrated using an academic and an industrially motivated example.

Period	05 Jul 2023
Event title	9th International Conference on Control, Decision and Information Technologies CoDIT'23
Event type	Conference
Location	ItalyShow on map