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.
|Title of host publication||2016 American Control Conference, ACC 2016|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2016 Jul 28|
|Event||2016 American Control Conference, ACC 2016 - Boston, United States|
Duration: 2016 Jul 6 → 2016 Jul 8
|Name||Proceedings of the American Control Conference|
|Other||2016 American Control Conference, ACC 2016|
|Period||16/7/6 → 16/7/8|
Bibliographical notePublisher Copyright:
© 2016 American Automatic Control Council (AACC).
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering