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Related Experiment Video

Updated: May 27, 2026

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

A simple control policy for achieving minimum jerk trajectories.

Mehrdad Yazdani¹, Geoffrey Gamble, Gavin Henderson

¹Electrical and Computer Engineering, University of California, San Diego, USA. myazdani@ucsd.edu

Neural Networks : the Official Journal of the International Neural Network Society

|December 6, 2011

Summary

This study introduces a bang-bang optimal control policy for fast, point-to-point hand movements, minimizing jerk for efficient and optimal biological control. This approach simplifies central nervous system implementation and reduces biomechanical strain.

Related Experiment Videos

Last Updated: May 27, 2026

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

Area of Science:

Motor Control
Biomechanics
Computational Neuroscience

Background:

Fast point-to-point hand movements, or ballistic movements, exhibit straight paths and bell-shaped velocity profiles.
Existing computational models often employ bang-bang control policies for movement, but without clear justification.

Purpose of the Study:

To propose and validate a bang-bang optimal control policy for achieving ballistic movements.
To demonstrate the optimality and physiological plausibility of this control strategy.

Main Methods:

Developed an optimal control policy minimizing the L∞ norm of the jerk profile for movements with defined start/end points and duration.
Compared the proposed control policy's output with human motion data acquired using a manipulandum.

Main Results:

The bang-bang optimal control policy successfully generates point-to-point fast hand movements.
The policy's predictions align with recorded human motion data.
The control strategy is computationally simple and minimizes biomechanical stress.

Conclusions:

Bang-bang control policies are optimal for generating ballistic movements, offering a computationally efficient strategy for the central nervous system.
This approach minimizes biomechanical wear and tear.
Physiological evidence supports the use of bang-bang control in biological systems.