Kernelized offset-free data-driven predictive control for nonlinear systems

TL;DR

This paper introduces a kernelized, offset-free data-driven predictive control method for nonlinear systems that improves tracking accuracy and stability by effectively learning unknown nonlinear dynamics.

Contribution

It proposes a novel kernel-based parameterization of nonlinear models enabling offset-free predictive control with stability guarantees.

Findings

Effective in reducing tracking offsets in nonlinear systems

Ensures recursive feasibility and stability

Demonstrated success on a nonlinear benchmark example

Abstract

This paper presents a kernelized offset-free data-driven predictive control scheme for nonlinear systems. Traditional model-based and data-driven predictive controllers often struggle with inaccurate predictors or persistent disturbances, especially in the case of nonlinear dynamics, leading to tracking offsets and stability issues. To overcome these limitations, we employ kernel methods to parameterize the nonlinear terms of a velocity model, preserving its structure and efficiently learning unknown parameters through a least squares approach. This results in a offset-free data-driven predictive control scheme formulated as a nonlinear program, but solvable via sequential quadratic programming. We provide a framework for analyzing recursive feasibility and stability of the developed method and we demonstrate its effectiveness through simulations on a nonlinear benchmark example.