Inferring human subject motor control intent using inverse MPC

Ahmed Ramadan, Jongeun Choi, Clark J. Radcliffe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)


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 languageEnglish
Title of host publication2016 American Control Conference, ACC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781467386821
Publication statusPublished - 2016 Jul 28
Event2016 American Control Conference, ACC 2016 - Boston, United States
Duration: 2016 Jul 62016 Jul 8

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2016 American Control Conference, ACC 2016
Country/TerritoryUnited States

Bibliographical note

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

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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