Determining human control intent using inverse LQR solutions

M. Cody Priess, Jongeun Choi, Clark Radcliffe

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

5 Citations (Scopus)


In this paper, we have developed a method for determining the control intention in human subjects during a prescribed motion task. Our method is based on the solution to the inverse LQR problem, which can be stated as: does a given controller K describe the solution to a time-invariant LQR problem, and if so, what weights Q and R produce K as the optimal solution? We describe an efficient Linear Matrix Inequality (LMI) method for determining a solution to the general case of this inverse LQR problem when both the weighting matrices Q and R are unknown. Additionally, we propose a gradient-based, leastsquares minimization method that can be applied to approximate a solution in cases when the LMIs are infeasible. We develop a model for an upright seated-balance task which will be suitable for identification of human control intent once experimental data is available.

Original languageEnglish
Title of host publicationAerial Vehicles; Aerospace Control; Alternative Energy; Automotive Control Systems; Battery Systems; Beams and Flexible Structures; Biologically-Inspired Control and its Applications;
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Print)9780791856123
Publication statusPublished - 2013
EventASME 2013 Dynamic Systems and Control Conference, DSCC 2013 - Palo Alto, CA, United States
Duration: 2013 Oct 212013 Oct 23

Publication series

NameASME 2013 Dynamic Systems and Control Conference, DSCC 2013


OtherASME 2013 Dynamic Systems and Control Conference, DSCC 2013
Country/TerritoryUnited States
CityPalo Alto, CA

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering


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