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 |
| Volume | 2016-July |
| ISBN (Electronic) | 9781467386821 |
| DOIs | |
| Publication status | Published - 2016 Jul 28 |
| Event | 2016 American Control Conference, ACC 2016 - Boston, United States Duration: 2016 Jul 6 → 2016 Jul 8 |
Other
| Other | 2016 American Control Conference, ACC 2016 |
|---|---|
| Country | United States |
| City | Boston |
| Period | 16/7/6 → 16/7/8 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
Cite this
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Inferring human subject motor control intent using inverse MPC. / Ramadan, Ahmed; Choi, Jongeun; Radcliffe, Clark J.
2016 American Control Conference, ACC 2016. Vol. 2016-July Institute of Electrical and Electronics Engineers Inc., 2016. p. 5791-5796 7526577.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
AU - Radcliffe, Clark J.
PY - 2016/7/28
Y1 - 2016/7/28
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.
UR - http://www.scopus.com/inward/record.url?scp=84992018133&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992018133&partnerID=8YFLogxK
U2 - 10.1109/ACC.2016.7526577
DO - 10.1109/ACC.2016.7526577
M3 - Conference contribution
AN - SCOPUS:84992018133
VL - 2016-July
SP - 5791
EP - 5796
BT - 2016 American Control Conference, ACC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -