An Online Adaptation Strategy for Direct Data-driven Control

TL;DR

This paper introduces an online data-driven control method that updates system models during operation, enabling better control of strongly nonlinear systems without requiring specific input excitation.

Contribution

It extends behavioral systems theory-based control to nonlinear systems by updating the system representation online without imposing excitation conditions.

Findings

Effective in a robotic arm simulation

Maintains data efficiency for nonlinear control

Operates as a parallel observer to the controller

Abstract

The fundamental lemma from behavioral systems theory yields a data-driven non-parametric system representation that has shown great potential for the data-efficient control of unknown linear and weakly nonlinear systems, even in the presence of measurement noise. In this work, we strive to extend the applicability of this paradigm to more strongly nonlinear systems by updating the system representation during control. Unlike existing approaches, our method does not impose suitable excitation to the control inputs, but runs as an observer parallel to the controller. Whenever a rank condition is deemed to be fulfilled, the system representation is updated using newly available datapoints. In a reference tracking simulation of a two-link robotic arm, we showcase the performance of the proposed strategy in a predictive control framework.