Inferring human subject motor control intent using inverse MPC

Ahmed Ramadan; Jongeun Choi; Clark J. Radcliffe

doi:10.1109/ACC.2016.7526577

Inferring human subject motor control intent using inverse MPC

Ahmed Ramadan, Jongeun Choi, Clark J. Radcliffe

Department of Mechanical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Citations (Scopus)

Abstract

The control intent is a strategy which a human subject follows to accomplish a certain motor control task such as controlling the trunk movement. The problem we address is how control theoretic approaches can capture such a strategy taking into account the physiological constraints and which approach is better.We present such an analysis, so-called intent-inferring. The control intent can be inferred by estimating the cost function that is minimized by a control system. We propose an inverse model predictive control (iMPC) algorithm to infer the control intent. We solve the iMPC problem for an illustrative example to evaluate the algorithm and to compare against inverse linear quadratic regulator (iLQR) approach. The simulated results show that our algorithm can recover the true cost function weights, and outperform the iLQR approach for the illustrative example.

Original language	English
Title of host publication	2016 American Control Conference, ACC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5791-5796
Number of pages	6
ISBN (Electronic)	9781467386821
DOIs	https://doi.org/10.1109/ACC.2016.7526577
Publication status	Published - 2016 Jul 28
Event	2016 American Control Conference, ACC 2016 - Boston, United States Duration: 2016 Jul 6 → 2016 Jul 8

Publication series

Name	Proceedings of the American Control Conference
Volume	2016-July
ISSN (Print)	0743-1619

Other

Other	2016 American Control Conference, ACC 2016
Country/Territory	United States
City	Boston
Period	16/7/6 → 16/7/8

Bibliographical note

Publisher Copyright:
© 2016 American Automatic Control Council (AACC).

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

Access to Document

10.1109/ACC.2016.7526577

Cite this

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title = "Inferring human subject motor control intent using inverse MPC",

abstract = "The control intent is a strategy which a human subject follows to accomplish a certain motor control task such as controlling the trunk movement. The problem we address is how control theoretic approaches can capture such a strategy taking into account the physiological constraints and which approach is better.We present such an analysis, so-called intent-inferring. The control intent can be inferred by estimating the cost function that is minimized by a control system. We propose an inverse model predictive control (iMPC) algorithm to infer the control intent. We solve the iMPC problem for an illustrative example to evaluate the algorithm and to compare against inverse linear quadratic regulator (iLQR) approach. The simulated results show that our algorithm can recover the true cost function weights, and outperform the iLQR approach for the illustrative example.",

author = "Ahmed Ramadan and Jongeun Choi and Radcliffe, {Clark J.}",

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doi = "10.1109/ACC.2016.7526577",

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Ramadan, A, Choi, J & Radcliffe, CJ 2016, Inferring human subject motor control intent using inverse MPC. in 2016 American Control Conference, ACC 2016., 7526577, Proceedings of the American Control Conference, vol. 2016-July, Institute of Electrical and Electronics Engineers Inc., pp. 5791-5796, 2016 American Control Conference, ACC 2016, Boston, United States, 16/7/6. https://doi.org/10.1109/ACC.2016.7526577

Inferring human subject motor control intent using inverse MPC. / Ramadan, Ahmed; Choi, Jongeun; Radcliffe, Clark J.

2016 American Control Conference, ACC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 5791-5796 7526577 (Proceedings of the American Control Conference; Vol. 2016-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Inferring human subject motor control intent using inverse MPC

AU - Ramadan, Ahmed

AU - Choi, Jongeun

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PY - 2016/7/28

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N2 - The control intent is a strategy which a human subject follows to accomplish a certain motor control task such as controlling the trunk movement. The problem we address is how control theoretic approaches can capture such a strategy taking into account the physiological constraints and which approach is better.We present such an analysis, so-called intent-inferring. The control intent can be inferred by estimating the cost function that is minimized by a control system. We propose an inverse model predictive control (iMPC) algorithm to infer the control intent. We solve the iMPC problem for an illustrative example to evaluate the algorithm and to compare against inverse linear quadratic regulator (iLQR) approach. The simulated results show that our algorithm can recover the true cost function weights, and outperform the iLQR approach for the illustrative example.

AB - The control intent is a strategy which a human subject follows to accomplish a certain motor control task such as controlling the trunk movement. The problem we address is how control theoretic approaches can capture such a strategy taking into account the physiological constraints and which approach is better.We present such an analysis, so-called intent-inferring. The control intent can be inferred by estimating the cost function that is minimized by a control system. We propose an inverse model predictive control (iMPC) algorithm to infer the control intent. We solve the iMPC problem for an illustrative example to evaluate the algorithm and to compare against inverse linear quadratic regulator (iLQR) approach. The simulated results show that our algorithm can recover the true cost function weights, and outperform the iLQR approach for the illustrative example.

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 American Control Conference, ACC 2016

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Inferring human subject motor control intent using inverse MPC

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