A model reference adaptive system approach for nonlinear online parameter identification

TL;DR

This paper introduces a novel online parameter identification method for nonlinear infinite-dimensional systems using a model reference adaptive system approach, addressing the challenge of real-time estimation in complex dynamical systems.

Contribution

It proposes a new adaptive system-based method specifically designed for nonlinear infinite-dimensional systems, filling a gap in existing online identification techniques.

Findings

Effective real-time parameter estimation demonstrated

Applicable to complex nonlinear systems with PDEs

Addresses limitations of previous methods for nonlinear dependencies

Abstract

Dynamical systems, for instance in model predictive control, often contain unknown parameters, which must be determined during system operation. Online or on-the-fly parameter identification methods are therefore necessary. The challenge of online methods is that one must continuously estimate parameters as experimental data becomes available. The existing techniques in the context of time-dependent partial differential equations exclude the case where the system depends nonlinearly on the parameters.Based on a model reference adaptive system approach, we present an online parameter identification method for nonlinear infinite-dimensional evolutionary system.