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

Feedback control systems01:26

Feedback control systems

657
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...
657
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

314
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
314
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

329
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
329
State Space Representation01:27

State Space Representation

496
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
496
Linear time-invariant Systems01:23

Linear time-invariant Systems

839
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
839
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

371
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
371

You might also read

Related Articles

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

Sort by
Same author

Mechanical loading primes MSC-derived exosomes to promote cartilage repair.

Bioactive materials·2026
Same author

Gut microbiota and its metabolites with thyroid diseases: functions and mechanisms.

Frontiers in immunology·2026
Same author

Predicting STAS in peripheral stage I lung adenocarcinoma: the incremental value of CT-based tumor disappearance rate, with a focus on part-solid nodules.

BMC medical imaging·2026
Same author

Standardized debridement for the management of fracture-related infection after intramedullary nailing: A retrospective single-center cohort study of 69 patients.

Medicine·2026
Same author

Electroacupuncture prevents CUMS induced depressive-like behaviors by inhibiting microglia-mediated synaptic pruning induced by gut dysbiosis.

Chinese medicine·2026
Same author

High-throughput screening of two-dimensional ferromagnetic materials with high Curie temperatures.

Nanoscale·2026

Related Experiment Video

Updated: Jan 7, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.0K

A nonsingular predefined time sliding mode control method with continuously variable exponents for nonlinear systems

Chao Jia1, Junjie Ping1

  • 1School of Electrical Engineering and Automation, Tianjin University of Technology, No.391 Binshui West Avenue, Xiqing District, Tianjin, China.

ISA Transactions
|January 3, 2026
PubMed
Summary

This study introduces a novel predefined-time nonsingular sliding mode control (SMC) method using emotional neural networks (ENN) for nonlinear systems. The approach ensures rapid stability and accurate trajectory tracking, outperforming existing methods.

Keywords:
Emotional neural networksNonlinear systems controlNonsingular controlPredefined time controlSliding mode control

More Related Videos

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.9K
Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.6K

Related Experiment Videos

Last Updated: Jan 7, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.0K
A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.9K
Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.6K

Area of Science:

  • Control Theory
  • Artificial Intelligence
  • Nonlinear Systems

Background:

  • Disturbed nonlinear systems present significant control challenges.
  • Traditional control methods often struggle with unknown dynamics and achieving rapid convergence.
  • Predefined-time stability (PDTS) offers a theoretical framework for guaranteed finite-time convergence.

Purpose of the Study:

  • To develop a novel predefined-time nonsingular sliding mode control (SMC) method.
  • To address challenges posed by disturbed nonlinear systems and unknown dynamics.
  • To enhance system stability and trajectory tracking performance.

Main Methods:

  • A new sufficient condition for predefined-time stability (PDTS) was constructed using Lyapunov stability theory.
  • An adaptive, continuously adjustable exponential term was introduced into the Lyapunov stability lemma.
  • Emotional Neural Networks (ENN) were employed to approximate the equivalent control term for nonsingular SMC design.

Main Results:

  • The proposed method rigorously proves system PDTS in both reaching and sliding phases.
  • Simulations demonstrated trajectory tracking for an inverted pendulum in 0.22 seconds.
  • Deep-sea vehicle manipulator simulations achieved trajectory tracking in 0.23 seconds, surpassing comparison methods.

Conclusions:

  • The proposed ENN-based predefined-time nonsingular SMC method effectively controls disturbed nonlinear systems.
  • The method achieves rapid and stable trajectory tracking with guaranteed predefined-time convergence.
  • This approach offers a significant improvement over existing control strategies for complex dynamic systems.