Feedback linearisation of mechanical systems using data-driven models

TL;DR

This paper introduces a data-driven feedback linearisation method for nonlinear mechanical systems, offering a robust alternative to traditional model-based approaches, demonstrated on synthetic and real systems.

Contribution

It presents a novel data-driven control approach that linearises nonlinear mechanical systems without requiring precise first-principles models.

Findings

High performance in linearising nonlinear responses

Robustness against model inaccuracies and extrapolation

Successful validation on synthetic and real systems

Abstract

Linearising the dynamics of nonlinear mechanical systems is an important and open research area. A common approach is feedback linearisation, which is a nonlinear control method that transforms the input-output response of a nonlinear system into an equivalent linear one. The main problem with feedback linearisation is that it requires an accurate first-principles model of the system, which are typically hard to obtain. In this paper, we design an alternative control approach that exploits data-driven models to linearise the input-output response of nonlinear mechanical systems. Specifically, a model-based reference tracking architecture is developed for nonlinear feedback systems with output nonlinearities. The overall methodology shows a high degree of performance combined with significant robustness against imperfect modelling and extrapolation. These findings are demonstrated using large set of synthetic experiments conducted on a asymmetric Duffing oscillator and using an experimental prototype of a high-precision motion system.