Adaptive Experiment Design for Nonlinear System Identification with Operational Constraints

TL;DR

This paper introduces an adaptive experiment design method for nonlinear system identification that updates in real-time, ensuring informative data collection while respecting operational constraints.

Contribution

It proposes a novel receding horizon approach with an adaptive input design criterion and a sequential estimator for online nonlinear system identification.

Findings

Effective online experiment design within operational constraints

Improved parameter estimation accuracy

Real-time adaptation to system dynamics

Abstract

We consider the joint problem of online experiment design and parameter estimation for identifying nonlinear system models, while adhering to system constraints. We utilize a receding horizon approach and propose a new adaptive input design criterion, which is tailored to continuously updated parameter estimates, along with a new sequential estimator. We demonstrate the ability of the method to design informative experiments online, while steering the system within operational constraints.