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

Abstract

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.

References in corpus (4)

• Deep learning for universal linear embeddings of nonlinear dynamics
• Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learning
• A Trajectory-Based Approach to Discrete-Time Flatness
• Data-Driven Feedback Linearization using the Koopman Generator

Cited by in corpus (1)

• A Dual Geometric Test for Forward-Flatness