A Control Theoretical Adaptive Human Pilot Model: Theory and Experimental Validation

TL;DR

This paper introduces an adaptive human pilot model based on control theory that can handle uncertainties, validated through experiments and statistical analysis for improved human-in-the-loop system modeling.

Contribution

It presents a novel adaptive pilot model using model reference adaptive control and Lyapunov stability, capable of mimicking human responses under uncertainties.

Findings

Model successfully mimics human pilot behavior.

Experimental data supports the model's predictive accuracy.

Applicable for stability and performance analysis in human-in-the-loop systems.

Abstract

This paper proposes an adaptive human pilot model that is able to mimic the crossover model in the presence of uncertainties. The proposed structure is based on the model reference adaptive control, and the adaptive laws are obtained using the Lyapunov-Krasovskii stability criteria. The model can be employed for human-in-the-loop stability and performance analyses incorporating different types of controllers and plant types. For validation purposes, an experimental setup is employed to collect data and a statistical analysis is conducted to measure the predictive power of the pilot model.