Learning controllers from data via kernel-based interpolation

2023/04/19 by Hu, Zhongjie, De Persis, Claudio, Tesi, Pietro · 3 citations
#FOS: Electrical engineering #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2304.09577

Abstract

We propose a data-driven control design method for nonlinear systems that builds on kernel-based interpolation. Under some assumptions on the system dynamics, kernel-based functions are built from data and a model of the system, along with deterministic model error bounds, is determined. Then, we derive a controller design method that aims at stabilizing the closed-loop system by cancelling out the system nonlinearities. The proposed method can be implemented using semidefinite programming and returns positively invariant sets for the closed-loop system.

Cited by

• Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches
• Stabilization of Nonlinear Systems with State-Dependent Representation: From Model-Based to Direct Data-Driven Control
• Data driven feedback linearization of nonlinear control systems via Lie derivatives and stacked regression approach

Related

• Learning Controllers from Data via Approximate Nonlinearity Cancellation
• Learning Controllers From Data via Approximate Nonlinearity Cancellation
• Kernel Methods for the Approximation of Nonlinear Systems
• Guarantees for data-driven control of nonlinear systems using semidefinite programming: A survey
• Robust and Kernelized Data-Enabled Predictive Control for Nonlinear Systems