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

One-Degree-of-Freedom System01:24

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...

You might also read

Related Articles

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

Sort by
Same author

DBHN-Net: Dual-Branch Hybrid Neural Network for Low-Complexity Monaural Speech Enhancement.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

A Multi-Modal Few-Shot Learning Framework for Foreign Object Segmentation in GIS Inspection.

Sensors (Basel, Switzerland)·2026
Same author

Deciphering oxidative stress-related heterogeneity and developing a prognostic signature for colorectal cancer.

Scientific reports·2026
Same author

Bridge Points Guided Neural Motion Planning in Complex Environments with Narrow Passages.

Sensors (Basel, Switzerland)·2026
Same author

[Thoughts and Prospects on Precision Control and Intelligent Positioning Robotic Systems Applied in Nasojejunal Feeding Tube Placement].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2025
Same author

Optimal trajectory tracking control of robotic manipulator system added by discrete-time fast terminal sliding mode predictive approach.

ISA transactions·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 27, 2026

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

Adaptive Neural Network-Based Tracking Control for a Single-Link Flexible Manipulator Under State Constraints.

Enrui Liu1, Wuxing Lai2, Songyi Dian2

  • 1Pittsburgh Institute, Sichuan University, Chengdu 610207, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

A new fractional-order adaptive neural network control scheme improves trajectory tracking for single-link flexible manipulators (SLFM). This method ensures system states remain bounded, outperforming existing control strategies.

Keywords:
barrier Lyapunov functionfractional order calculusradial basis function neural networksingle-link flexible manipulator

More Related Videos

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
06:52

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

Design and Implementation of a Bespoke Robotic Manipulator for Extra-corporeal Ultrasound
07:41

Design and Implementation of a Bespoke Robotic Manipulator for Extra-corporeal Ultrasound

Published on: January 7, 2019

Related Experiment Videos

Last Updated: Jun 27, 2026

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
06:52

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

Design and Implementation of a Bespoke Robotic Manipulator for Extra-corporeal Ultrasound
07:41

Design and Implementation of a Bespoke Robotic Manipulator for Extra-corporeal Ultrasound

Published on: January 7, 2019

Area of Science:

  • Robotics and Control Systems
  • Nonlinear Dynamics
  • Fractional Calculus

Background:

  • Flexible manipulators offer advantages in lightweight design and energy efficiency for precision tasks.
  • Controlling flexible manipulators is challenging due to inherent nonlinearities and system uncertainties.

Purpose of the Study:

  • To develop a robust control scheme for trajectory tracking of a single-link flexible manipulator (SLFM) under symmetric time-varying full-state constraints.
  • To address the control challenges posed by nonlinearities and uncertainties in SLFM systems.

Main Methods:

  • Established a fractional-order dynamic model to capture SLFM characteristics.
  • Developed an adaptive radial basis function (RBF) neural network control within a backstepping framework.
  • Incorporated a symmetric time-varying barrier Lyapunov function (BLF) and command filters to ensure state constraints and avoid complexity explosion.

Main Results:

  • Theoretical analysis confirmed boundedness of all closed-loop system signals and convergence of tracking error.
  • Simulations demonstrated excellent control performance with tracking error < 0.02 rad and tip polarization angle < 0.045 rad.
  • The proposed controller showed superior performance with less tracking error compared to DSC and SMC methods.

Conclusions:

  • The fractional-order adaptive neural network control scheme effectively manages SLFM trajectory tracking under constraints.
  • The method validates the effectiveness and superiority of the proposed control strategy for flexible manipulator systems.