Paper Info
Title
Data-Driven Human Modeling: Quantifying Personal Tendency Toward Laziness
Abstract
This letter addresses the modeling of a personal tendency by utilizing the data collected from a manned control system. In the control system, it is assumed that a control operator, namely a human controller, determines the control actions based on his/her tendency toward laziness. The tendency is described by a cost function that includes the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm of the state and the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm of the control action. Then, the operator behavior is modeled by the solution to the optimization problem formulated with the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -state/L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -action cost function and the plant model. The tendency modeling is reduced to the problem of estimating the cost function. The estimation problem is further extended by taking into account the operator dynamics caused by the recognition and motion to derive an MPC-based formulation. Finally, the estimation method is demonstrated via an actual manned control experiment with a toy game.
Year
DOI
Venue
2021
10.1109/LCSYS.2020.3023337
IEEE Control Systems Letters
Keywords
DocType
Volume
Human tendency modeling,L₂/L₁ optimal control,model predictive control
Journal
5
Issue
ISSN
Citations 
4
2475-1456
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Keita Hara100.34
Masaki Inoue200.34
Jose Maria Maestre33214.98