Identification For Control Based on Neural Networks: Approximately Linearizable Models

TL;DR

This paper introduces a neural network-based identification method for nonlinear systems that produces models approximately linearizable by feedback, simplifying control design and stability analysis.

Contribution

It proposes a control-oriented neural network identification scheme that ensures models are approximately linearizable, enabling straightforward control design using linear control theory.

Findings

Effective on benchmark system identification tasks

Models are approximately linearizable by feedback

Facilitates robust control design and stability analysis

Abstract

This work presents a control-oriented identification scheme for efficient control design and stability analysis of nonlinear systems. Neural networks are used to identify a discrete-time nonlinear state-space model to approximate time-domain input-output behavior of a nonlinear system. The network is constructed such that the identified model is approximately linearizable by feedback, ensuring that the control law trivially follows from the learning stage. After the identification and quasi-linearization procedures, linear control theory comes at hand to design robust controllers and study stability of the closed-loop system. The effectiveness and interest of the methodology are illustrated throughout the paper on popular benchmarks for system identification.