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...

Design Example: Creating a Hydraulic Model of a Dam Spillway

Design Example: Creating a Hydraulic Model of a Dam Spillway

Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.

Root-Locus Method

Root-Locus Method

A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block...

Time-Domain Interpretation of PD Control

Time-Domain Interpretation of PD Control

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...

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...

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,...

You might also read

Related Articles

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

Sort by

Same author

Changes in corneal astigmatism, axial length and anterior chamber depth after vitrectomy and phacovitrectomy in rhegmatogenous retinal detachment.

International ophthalmology·2026

Same author

Consensus sequence engineering of potato patatin: Enhancing recombinant production and functional properties.

Bioresource technology·2026

Same author

Determination of Small-Sized α-Synuclein Oligomers Using Solid-State Nanopore Assisted with Circular Single-Stranded DNA Frame.

Analytical chemistry·2026

Same author

Molecular Evidence from FT-ICR MS and Bayesian Mixing Model Reveals Hillslope Soils as Dominant DOM Contributors during Rainfall.

Environmental science & technology·2026

Same author

Matrix stiffness disrupts tight junction integrity in retinal pigment epithelial cells via YAP1-mediated autophagy suppression.

Experimental cell research·2026

Same author

Interpretable machine learning for the grade prediction of strong flavor <i>yuanjiu</i> (crude Baijiu) based on HS-GC-IMS.

Food chemistry: X·2026

Search research articles

Related Experiment Video

Updated: Jan 10, 2026

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

Funnel control based on unknown system dynamics estimator for hydraulically-driven lower limb exoskeleton robot.

Jinsong Zhao¹, Huidong Hou², Yunpeng Zhang²

¹School of Mechanical Engineering, Yanshan University, Qinhuangdao, 066004, China; Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao, 066004, China; Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University), Ministry of Education of China, Qinhuangdao, 066004, China; State Key Laboratory of Crane Technology, Yanshan University, Qinhuangdao, 066004, China.

ISA Transactions

|November 19, 2025

Summary

This summary is machine-generated.

This study introduces a novel funnel control (FC) strategy with an unknown system dynamics estimator (USDE) to improve trajectory tracking for hydraulically-driven lower limb exoskeleton robots (HDLLERs). The method effectively compensates for uncertainties and disturbances, enhancing robot performance.

Keywords:

Funnel control Human-robot coupling Hydraulic-driven lower limb exoskeleton robot Unknown system dynamics estimator

More Related Videos

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

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

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

Published on: July 22, 2014

Related Experiment Videos

Last Updated: Jan 10, 2026

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

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

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

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

Published on: July 22, 2014

Area of Science:

Robotics
Control Systems Engineering
Biomechanics

Background:

Hydraulically-driven lower limb exoskeleton robots (HDLLERs) enhance human mobility but face challenges in precise control.
Human-robot coupling and system uncertainties hinder accurate trajectory tracking.

Purpose of the Study:

To develop a robust control strategy for HDLLERs that addresses unknown dynamics and external disturbances.
To improve the trajectory tracking precision and overall performance of lower limb exoskeleton robots.

Main Methods:

A novel funnel control (FC) strategy integrated with an unknown system dynamics estimator (USDE) was proposed.
The HDLLER dynamic model was transformed into a Brunovsky canonical form to manage complexity.
High gain observers (HGO) were used for state reconstruction, and a modified funnel function constrained tracking errors.

Main Results:

The proposed FC-USDE strategy effectively compensated for unknown internal and external dynamic influences.
Simulations and experiments demonstrated improved transient and steady-state performances in trajectory tracking.
The method successfully managed the 'explosion of complexity' in high-order systems.

Conclusions:

The developed funnel control strategy with an unknown system dynamics estimator offers a robust solution for precise trajectory tracking in HDLLERs.
The approach enhances exoskeleton robot performance by mitigating uncertainties and disturbances.
Validated through simulations and gait experiments, the method shows significant potential for exoskeleton applications.