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

Electro-mechanical Systems01:19

Electro-mechanical Systems

934
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...
934
Open and closed-loop control systems01:17

Open and closed-loop control systems

698
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
698
Feedback control systems01:26

Feedback control systems

297
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
297
Control Systems01:10

Control Systems

1.1K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.1K
PD Controller: Design01:26

PD Controller: Design

203
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,...
203
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

86
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
86

You might also read

Related Articles

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

Sort by
Same author

Fly motion vision maximizes signal energy transfer between mechanical input and sensor output.

Science robotics·2026
Same author

Neural dynamics of robust legged robots.

Frontiers in robotics and AI·2024
Same author

Distributed IMU Sensors for In-Field Dynamic Measurements on an Alpine Ski.

Sensors (Basel, Switzerland)·2024
Same author

Lidar-Based Navigation of Subterranean Environments Using Bio-Inspired Wide-Field Integration of Nearness.

Sensors (Basel, Switzerland)·2022
Same author

Spider-Inspired Electrohydraulic Actuators for Fast, Soft-Actuated Joints.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2021
Same author

Design of a High-Speed Prosthetic Finger Driven by Peano-HASEL Actuators.

Frontiers in robotics and AI·2021

Related Experiment Video

Updated: Jun 16, 2025

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot
07:40

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

Published on: June 10, 2020

13.9K

Robust control of electrohydraulic soft robots.

Angella Volchko1, Shane K Mitchell2, Tyler G Scripps1

  • 1Paul M. Rady Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, United States.

Frontiers in Robotics and AI
|August 19, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a robust control framework for electrohydraulic soft robots using linear control theory on nonlinear systems. The approach enables precise control of complex soft robotic systems, enhancing real-world applications.

Keywords:
H infinity synthesisHASEL actuatorslinear system theoryrobust controlsoft roboticsuncertainty analysis

More Related Videos

Fabrication of Soft Pneumatic Network Actuators with Oblique Chambers
07:09

Fabrication of Soft Pneumatic Network Actuators with Oblique Chambers

Published on: August 17, 2018

9.0K
Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery
11:06

Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery

Published on: November 14, 2015

8.9K

Related Experiment Videos

Last Updated: Jun 16, 2025

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot
07:40

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

Published on: June 10, 2020

13.9K
Fabrication of Soft Pneumatic Network Actuators with Oblique Chambers
07:09

Fabrication of Soft Pneumatic Network Actuators with Oblique Chambers

Published on: August 17, 2018

9.0K
Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery
11:06

Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery

Published on: November 14, 2015

8.9K

Area of Science:

  • Robotics
  • Control Systems Engineering
  • Mechanical Engineering

Background:

  • Soft robots offer unique advantages but present significant control challenges due to their inherent nonlinearities and uncertainties.
  • Existing control methods often struggle with the complexity and compliance of soft robotic systems, limiting their real-world applicability.

Purpose of the Study:

  • To introduce a model-based robust control framework for electrohydraulic soft robots.
  • To develop a method for creating accurate linear models of nonlinear soft robotic systems from empirical data.
  • To design an optimal controller that ensures robust performance despite system uncertainties.

Main Methods:

  • Utilized dynamic mode decomposition with control (DMDc) to derive linear models from system measurements.
  • Developed multiple linear models across different operational regions to facilitate uncertainty analysis.
  • Employed H-infinity synthesis techniques to design a robust controller for the nominal plant.
  • Applied the framework to a multi-input multi-output (MIMO) hydraulically amplified self-healing electrostatic (HASEL)-actuated system.

Main Results:

  • Successfully demonstrated robust control over a complex MIMO HASEL-actuated soft robot.
  • The framework effectively simplifies the control of nonlinear soft robotic systems.
  • The developed linear models accurately represent system dynamics in various operational states.

Conclusions:

  • The proposed model-based robust control framework provides a flexible and effective approach for real-time control of soft robotic systems.
  • This methodology addresses the inherent complexities and uncertainties in compliant robots, paving the way for advanced real-world applications.
  • The integration of linear control theory with nonlinear soft robot dynamics offers a promising direction for future research and development in soft robotics.