Skip to main content

A neuromuscular model featuring co-activation for use in driver simulation

Buy Article:

$55.00 plus tax (Refund Policy)

This paper covers the development of a neuromuscular model for use in driver simulation, focusing on the inclusion of a representation of co-activation. Co-activation is thought to function as a combination of feed-forward control (based on future predictions of the task being undertaken) and feedback control (to reject disturbances). A linear model of the neuromuscular system, muscles, limbs and vehicle was set up. Linear quadratic regulator control was used to minimise path-following error and a representation of the muscle's metabolic energy consumption. It is shown that the model can be used to generate feed-forward control signals whilst simultaneously minimising the feedback error signal (necessary in real muscles for effective disturbance rejection), but that there is a trade-off between minimisation of the feedback error signal and energy consumption. The controller is able to adapt to the increases in reflex delay and gain to maintain control using the feed-forward mechanism. Large reflex gain and delay increases are shown to destabilise the system, consistent with suggestions that, in humans, the reflex gain is small to avoid instability. The model is shown to be capable of rejecting external disturbances via the stretch reflex.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: co-activation; driver modelling; metabolic energy; neuromuscular system; stretch reflex dynamics

Document Type: Research Article

Affiliations: Department of Engineering, University of Cambridge, Cambridge, UK

Publication date: 01 September 2008

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more