Model Completion through Nonlinear System Identification

Introduction

Systems and control engineers aim to master increasingly complex dynamical systems while including stronger performance, operational, and energy constraints. As model-based control design remains the dominant paradigm, this results in an increasing need for nonlinear modeling. However, model interpretability and generalization capabilities form important roadblocks for a wide adaptation and applicability of nonlinear system identification methods.

Existing Models

Strong prior knowledge is given by existing models, provided by system designers and engineers, even though they do not capture all the nonlinear dynamics of the real-life system. These models are currently not accounted for during black-box system identification.

Project Objectives

COMPLETE aims to develop a comprehensive nonlinear system identification framework to obtain accurate and interpretable models of measured complex system dynamics by completing an approximate pre-existing model through black-box nonlinear system identification. The project focuses on the following objectives:

1. Provide model structures, algorithms, and theory that flexibly interconnect the pre-existing model and the black-box completion.
2. Ensure that data-driven completion models are interpretable and preserve key system theoretic aspects.
3. Develop data-driven experiment design strategies to detect, quantify, and localize model errors at low experimental cost.

Significance

These objectives are far beyond the actual abilities of system identification, lifting the model completion for dynamical systems from ad-hoc approaches to a systematic, flexible, theoretically supported framework.

Expertise and Applicability

My leading expertise in structured nonlinear system identification, along with recent proof-of-concept results, ensures the feasibility of the project. The resulting system identification framework is applicable across a wide range of engineering disciplines (mechanical, electrical, biomedical) and provides system engineers with the necessary insight to guide them towards better solutions for tomorrow's industry.