Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

PD Controller: Design01:26

PD Controller: Design

687
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
687
Electro-mechanical Systems01:19

Electro-mechanical Systems

1.7K
Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...
1.7K
Controller Configurations01:22

Controller Configurations

415
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
415

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Proprioceptive Feedback Control Improves Peristaltic Turning in Confined Environments.

Bioinspiration & biomimetics·2026
Same author

Resin models of Drosophila strain sensors highlight mechanical pre-filtering of sensory inputs.

Bioinspiration & biomimetics·2026
Same author

Bioinspired additive manufacturing material optimization for increased stiffness and improved strain sensing in robotic limbs.

Bioinspiration & biomimetics·2026
Same author

Brain-inspired energy efficient technologies for next-generation artificial intelligence.

Biological cybernetics·2026
Same author

A 3D model predicts behavior of a soft bodied worm robot performing peristaltic locomotion.

Bioinspiration & biomimetics·2025
Same author

Neuromechanical Simulation with NEURON and MuJoCo.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: Mar 7, 2026

Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.7K

Design process and tools for dynamic neuromechanical models and robot controllers.

Nicholas S Szczecinski1, Alexander J Hunt2, Roger D Quinn2

  • 1, 10900 Euclid Ave., Cleveland, OH, 44106, USA. nicholas.szczecinski@case.edu.

Biological Cybernetics
|February 23, 2017
PubMed
Summary
This summary is machine-generated.

We developed a three-step design process and tools to optimize parameters for robot controllers. This method enables robust posture and locomotion control in simulations, applicable to various robotic systems.

Keywords:
Central pattern generatorLocomotionRoboticsSensory feedbackStability

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.9K
Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
11:16

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

16.7K

Related Experiment Videos

Last Updated: Mar 7, 2026

Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.7K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.9K
Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
11:16

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

16.7K

Area of Science:

  • Robotics
  • Computational Neuroscience
  • Control Systems Engineering

Background:

  • Developing effective controllers for robotic posture and locomotion is complex.
  • Neuromechanical simulations require precise parameter tuning for realistic behavior.

Purpose of the Study:

  • To present a serial design process and associated tools for selecting parameter values for robot controllers.
  • To enable robust and stable control of simulated robot posture and locomotion.

Main Methods:

  • Utilized dynamic neuron and synapse models within the AnimatLab 2 simulator.
  • Employed a three-step design process involving tools: FEEDBACKDESIGN, CPGDESIGN, and SIMSCAN.
  • FEEDBACKDESIGN uses classical control for servomotor stability; CPGDESIGN analyzes central pattern generator (CPG) oscillations and entrainment; SIMSCAN performs batch simulations for parameter space exploration.

Main Results:

  • FEEDBACKDESIGN rapidly identifies stable neural and synaptic parameters.
  • CPGDESIGN reveals parameters for robust CPG oscillations and sensory entrainment, optimizing inter-joint pathways.
  • SIMSCAN facilitates understanding parameter sensitivity and the effects of descending commands on locomotion.

Conclusions:

  • The presented design process and tools offer an efficient method for parameter selection in neuromechanical controllers.
  • These tools are demonstrated on a robot simulation and are adaptable for neuromechanical animal models and physical robots.