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

Second Order systems I01:20

Second Order systems I

220
A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
220
Controller Configurations01:22

Controller Configurations

142
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...
142
Feedback control systems01:26

Feedback control systems

397
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...
397
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

546
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...
546
PID Controller01:19

PID Controller

223
Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
223
Open and closed-loop control systems01:17

Open and closed-loop control systems

948
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...
948

You might also read

Related Articles

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

Sort by
Same author

Evaluation of <i>Ganoderma lucidum</i> Across Varieties and Growth Stages: Integrating Chromatographic Profiling, Bioactivity Correlation, and In Silico Simulations.

Foods (Basel, Switzerland)·2026
Same author

Exogenous quorum sensing signal enhances central energy metabolism to fuel biofilm formation and denitrification on microplastics.

Journal of hazardous materials·2026
Same author

Clinical Profiles and Therapeutic Interventions in Occult Paraquat Poisoning: A Comparative Report of Two Cases.

Clinical case reports·2026
Same author

Global land rush concentrates potential zoonotic spillover risk in the tropics.

Communications sustainability·2026
Same author

Loganin alleviates oxidative stress-induced apoptosis by modulating mitochondrial function and STAT3 signaling in DSS-induced colitis and H<sub>2</sub>O<sub>2</sub>-injured Caco-2 cells.

Scientific reports·2026
Same author

The <i>Ascosphaera apis</i> Invasion of <i>Apis cerana</i> Worker Larvae: Long Non-Coding RNA-Mediated Regulation.

Biology·2026

Related Experiment Video

Updated: Aug 30, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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

1.7K

RBFNN-Enabled Adaptive Parameters Identification for Robot Servo System Based on Improved Sliding Mode Observer.

Ye Li1, Dazhi Wang1, Mingtian Du2

  • 1School of Information Science and Engineering, Northeastern University, Shenyang 110819, China.

Computational Intelligence and Neuroscience
|September 1, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive sliding mode observer (SMO) to accurately identify robot servo system load torque. The improved SMO reduces chattering for enhanced control performance and parameter estimation.

More Related Videos

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.8K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

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

Published on: October 14, 2017

11.8K

Related Experiment Videos

Last Updated: Aug 30, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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

1.7K
An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.8K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

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

Published on: October 14, 2017

11.8K

Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Accurate load torque identification is crucial for robot servo system control performance.
  • Sliding mode observers (SMO) are effective but suffer from chattering, reducing accuracy.
  • Chattering in SMOs leads to decreased load torque identification accuracy and impacts overall system performance.

Purpose of the Study:

  • To propose an adaptive parameter identification method using an improved sliding mode observer (SMO).
  • To enhance the accuracy and robustness of load torque identification in robot servo systems.
  • To mitigate the chattering phenomenon inherent in traditional SMOs.

Main Methods:

  • Implemented a continuous saturation function for the SMO switching function to reduce chattering.
  • Defined the relationship between sliding mode and feedback gains for optimal selection.
  • Utilized a radial basis function neural network (RBFNN) for adaptive boundary layer gain tuning.
  • Introduced a variable period integration method for identifying moment of inertia and refining load torque calculation.

Main Results:

  • The improved SMO effectively reduces chattering, leading to smoother and more accurate load torque estimation.
  • Adaptive tuning of the boundary layer gain by RBFNN improved identification accuracy across varying speeds.
  • The variable period integration method successfully addressed inertia mismatch errors.
  • Simulation experiments validated the proposed method's effectiveness and superiority over traditional approaches.

Conclusions:

  • The proposed adaptive SMO, incorporating observer gain tuning and inertia matching, provides a robust and accurate method for load torque estimation.
  • This adaptive identification approach offers a valuable reference for improving robot servo system control.
  • The method successfully mitigates chattering and enhances parameter identification accuracy, paving the way for better robot performance.