Data-Driven Human Modeling: Quantifying Personal Tendency Toward Laziness

Keita Hara, Masaki Inoue, Jose Maria Maestre

Research output: Contribution to journalArticlepeer-review

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 L2 norm of the state and the L1 norm of the control action. Then, the operator behavior is modeled by the solution to the optimization problem formulated with the L2-state/L1-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.

Original languageEnglish
Article number9193972
Pages (from-to)1219-1224
Number of pages6
JournalIEEE Control Systems Letters
Volume5
Issue number4
DOIs
Publication statusPublished - 2021 Oct

Keywords

  • Human tendency modeling
  • L2/L1 optimal control
  • model predictive control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization

Fingerprint Dive into the research topics of 'Data-Driven Human Modeling: Quantifying Personal Tendency Toward Laziness'. Together they form a unique fingerprint.

Cite this