System Identification and Model-based Robust Nonlinear Disturbance Rejection Control

TL;DR

This paper develops a systematic, model-based approach for nonlinear disturbance rejection control, focusing on advanced modeling of nonlinearities and uncertainties in dynamic systems to improve robustness.

Contribution

It introduces a new local polynomial approach extending subspace algorithms for nonlinear system modeling and addresses disturbance control challenges in complex systems.

Findings

Extended subspace algorithms for nonlinear systems

Enhanced modeling of uncertainties and high-order dynamics

Improved robustness in disturbance rejection control

Abstract

The robust disturbance rejection controller has been the subject of intensive research due to its undeniable importance for automation. Modern control theory tends to use model-based approaches versus model-free approaches, especially when it comes to highly modern applications. The backbone of the dissertation is based on the systematic modeling of dynamic systems and the development of advanced control methods. Accordingly, the dissertation begins with the investigation of nonlinearities in dynamic systems. The extension of classic subspace algorithms for linear systems in the frequency domain is tackled using the new local polynomial approach. Next, the problem of disturbance control is addressed, namely modeling of uncertainties and non-modeled high-order dynamics, fragility of the controller and observer systems, and the non-linearities are analyzed separately.