A Predictive Model of Human Movements based on Model Predictive Control for Human-Robot Interaction

Aeden G. Gillam, Reza Sharif Razavian

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

Abstract

Predicting human movements is vital to safely control robots that physically interact with humans. However, predictive neuromuscular models that are fast enough for realtime control applications have proven challenging, due to the complexity of the neural and musculoskeletal systems. Nonlinear optimization-based prediction of movements in a musculoskeletal model is prohibitively slow. On the other hand, highly simplified models based on linear control theory cannot handle complexities of the human musculoskeletal system. Model Predictive Control (MPC) can potentially fill the gap between these two modeling extremes, by taking into account physiological nonlinearities, constraints, and redundancies while keeping computations fast through its receding horizon formulation. This study presents a new predictive model for the human movements based on MPC, which can control activity of four muscles acting on an inertia in a two-dimensional space to generate movements. The MPC results are compared to that of the prominent human motor control model in the neuroscience literature, which is based on linear quadratic regulator. The predicted movements are similar between the two controllers and are qualitatively similar to human behavior. MPC achieves these results while satisfying physiological constraints on muscle activities and ranges of motion - features that are not present in the existing models. These results demonstrate promise and potential for MPC controllers to accurately predict human neuro-muscular activities for the next generation controllers for human-robot interaction.

Original languageEnglish (US)
Title of host publication2024 IEEE International Conference on Advanced Intelligent Mechatronics, AIM 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages82-87
Number of pages6
ISBN (Electronic)9798350355369
DOIs
StatePublished - 2024
Externally publishedYes
Event2024 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2024 - Boston, United States
Duration: Jul 15 2024Jul 19 2024

Publication series

NameIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
ISSN (Print)2159-6247
ISSN (Electronic)2159-6255

Conference

Conference2024 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2024
Country/TerritoryUnited States
CityBoston
Period7/15/247/19/24

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications
  • Software

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