Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Open and closed-loop control systems

Open and closed-loop control systems

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...

PD Controller: Design

PD Controller: Design

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,...

One-Degree-of-Freedom System

One-Degree-of-Freedom System

In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...

Feedback control systems

Feedback control systems

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...

You might also read

Related Articles

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

Sort by

Same author

Adaptive Control and Optimal Trajectory Generation for Highly Dynamic Tasks on a Soft Robot.

Soft robotics·2025

Same author

Understanding human co-manipulation via motion and haptic information to enable future physical human-robotic collaborations.

Frontiers in neurorobotics·2025

Same author

A Force/Torque Taxonomy for Classifying States During Physical Co-Manipulation.

IEEE transactions on haptics·2025

Same author

Human-robot planar co-manipulation of extended objects: data-driven models and control from human-human dyads.

Frontiers in neurorobotics·2024

Same author

Design Optimization for Rough Terrain Traversal Using a Compliant, Continuum-Joint, Quadruped Robot.

Frontiers in robotics and AI·2022

Same author

Performance Metrics for Fluidic Soft Robot Rotational Actuators.

Frontiers in robotics and AI·2021

Search research articles

Related Experiment Video

Updated: Nov 19, 2025

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

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

Published on: June 10, 2020

Model Reference Predictive Adaptive Control for Large-Scale Soft Robots.

Phillip Hyatt¹, Curtis C Johnson¹, Marc D Killpack¹

¹Robotics and Dynamics Lab, Department of Mechanical Engineering, Brigham Young University, Provo, UT, United States.

Frontiers in Robotics and AI

|January 27, 2021

Summary

This study introduces a new adaptive model predictive control (MPAC) for soft robots, improving accuracy and robustness over traditional methods. Experiments show MPAC outperforms model predictive control (MPC) and model reference adaptive control (MRAC).

Keywords:

MRAC adaptive control continuum robot dynamic modeling model predictive control parameter mismatch soft robot structure mismatch

More Related Videos

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

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

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

Published on: November 14, 2015

Related Experiment Videos

Last Updated: Nov 19, 2025

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

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

Published on: June 10, 2020

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

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

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

Published on: November 14, 2015

Area of Science:

Robotics
Control Systems
Soft Materials

Background:

Model predictive control (MPC) is effective for soft robots but often requires integral control due to model inaccuracies.
Existing lumped-parameter models for continuum joint soft robots have limitations in accuracy and computational efficiency.

Purpose of the Study:

To present a novel, accurate, yet computationally tractable dynamic model for continuum joint soft robots.
To introduce an adaptive model predictive control (MPAC) strategy for enhanced robot control.
To evaluate the performance of MPAC against traditional MPC and MRAC.

Main Methods:

Developed a new dynamic model based on piecewise constant curvature (PCC) and efficient kinematic representation.
Implemented model reference predictive adaptive control (MRPAC) for online model adaptation.
Conducted experiments in simulation and hardware to validate the proposed method.

Main Results:

The novel PCC-based model offers improved accuracy while remaining suitable for fast MPC.
MRPAC demonstrated robustness to parameter mismatch (e.g., unknown inertia) and structure mismatch (e.g., unmodeled disturbances).
Experimental results showed MRPAC outperformed both MPC and MRAC in performance.

Conclusions:

The proposed dynamic model and MRPAC strategy significantly enhance the control of continuum joint soft robots.
MRPAC provides a robust solution for soft robot control, addressing limitations of existing methods.
This adaptive control approach offers a promising direction for future soft robotics research.