Data-Driven Closed-Loop Reachability Analysis for Nonlinear Human-in-the-Loop Systems Using Gaussian Mixture Model

This article presents data-driven algorithms to perform the reachability analysis of nonlinear human-in-the-loop (HITL) systems. Such systems require consideration of the human control policy, otherwise might result in a conservative reachable set. However, formulating the human control policy in a...

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Bibliographic Details
Published inIEEE transactions on control systems technology Vol. 33; no. 2; pp. 788 - 798
Main Authors Choi, Joonwon, Byeon, Sooyung, Hwang, Inseok
Format Journal Article
LanguageEnglish
Published New York IEEE 01.03.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Accuracy
Algorithms
Analytical models
Closed loops
Control systems
Data-driven modeling
Heuristic algorithms
Hierarchies
human-in-the-loop (HITL)
Nonlinear dynamical systems
Nonlinear systems
Prediction algorithms
Probabilistic models
Reachability analysis
Stochastic processes
Trajectory
Vehicle dynamics
vehicle safety
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Summary:This article presents data-driven algorithms to perform the reachability analysis of nonlinear human-in-the-loop (HITL) systems. Such systems require consideration of the human control policy, otherwise might result in a conservative reachable set. However, formulating the human control policy in a mathematically tractable form is challenging, and thus, it is commonly ignored or simplified in many applications. To tackle this problem, we propose Gaussian mixture model (GMM)-based data-driven algorithms that can explicitly consider the human control policy during the reachability analysis of an HITL system. The proposed algorithms learn the human control policy as a GMM using the given trajectory. Then, the control input from the human operator is predicted based on the trained GMM by leveraging the Gaussian mixture regression (GMR), thereby facilitating the closed-loop forward stochastic reachability analysis. In this article, we examine two types of human control policies, state-independent and state-dependent, and propose the respective algorithms. We also tested our proposed algorithms using the human subject experimental data and demonstrated to generate more accurate results compared with other existing algorithms.
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ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2024.3518118