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

332
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...
332
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

106
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....
106
Open and closed-loop control systems01:17

Open and closed-loop control systems

785
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...
785
Transfer Function in Control Systems01:21

Transfer Function in Control Systems

548
The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
To derive the transfer function, consider a general nth-order linear time-invariant...
548
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

96
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,...
96
Control Systems01:10

Control Systems

1.2K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Synergetic Learning Neuro-Control for Unknown Affine Nonlinear Systems With Asymptotic Stability Guarantees.

IEEE transactions on neural networks and learning systems·2024
Same author

Efficacy of add-on blonanserin in treatment-resistant schizophrenia therapy: A retrospective cohort study.

Asian journal of psychiatry·2023
Same author

Multiple therapies relieve long-term tardive dyskinesia in a patient with chronic schizophrenia: A case report.

World journal of clinical cases·2023
Same author

Internet appointment has more advantages than traditional appointment in the nursing service of dry eye patients.

Medicine·2023
Same author

Dynamic assessment of dust hazard risk in the reconstruction of old industrial buildings: coupling effects of dust distribution and personnel trajectories.

Environmental science and pollution research international·2023
Same author

Performance evaluation of PLT-H (hybrid-channel platelet) under various interferences and application studies for platelet transfusion decisions.

Platelets·2023
Same journal

Dynamic analysis and reliable mechanical optimization application of ring HNN effected with a memristive neuron.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

DAFF-Net: A detection and search method for small-scale low surface brightness galaxies.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Quasi-synchronization for complex networks with hybrid pinning intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Physics-encoded convolutional neural operators for parametric PDEs: A convergence-guaranteed framework via pre-computed kernel fields.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: Jul 15, 2025

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

Synergetic learning for unknown nonlinear H∞ control using neural networks.

Liao Zhu1, Ping Guo1, Qinglai Wei2

  • 1International Academic Center of Complex Systems, Beijing Normal University, Zhuhai, 519087, Guangdong, China; School of Systems Science, Beijing Normal University, Beijing, 100875, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel data-driven H-infinity control method using synergetic learning for unknown nonlinear systems. The algorithm achieves real-time, robust control by learning optimal policies through a model-free Hamilton-Jacobi-Isaacs equation.

Keywords:
Adaptive dynamic programmingData-drivenH(∞) controlNeural networkNonlinear systemsTemporal difference

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
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

4.4K

Related Experiment Videos

Last Updated: Jul 15, 2025

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
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
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

4.4K

Area of Science:

  • Control Theory
  • Machine Learning
  • Nonlinear Systems

Background:

  • H-infinity control offers robustness but struggles with unknown nonlinear systems.
  • Existing methods lack real-time adaptation for complex, unpredictable environments.

Purpose of the Study:

  • Develop an online, real-time synergetic learning algorithm for data-driven H-infinity control.
  • Address robustness challenges in completely unknown affine nonlinear systems.

Main Methods:

  • Formulated H-infinity control as a two-player zero-sum game.
  • Derived a model-free Hamilton-Jacobi-Isaacs equation (MF-HJIE) using off-policy reinforcement learning.
  • Employed temporal difference learning and experience replay for online optimization.

Main Results:

  • Proved equivalence between MF-HJIE and conventional HJIE.
  • Demonstrated uniform ultimate boundedness of the synergistic learning system.
  • Validated the method's tractability via simulations on F16 aircraft and nonlinear systems.

Conclusions:

  • The proposed synergetic learning algorithm enables effective data-driven H-infinity control for unknown nonlinear systems.
  • The method provides real-time, robust control solutions adaptable to dynamic environments.
  • Simulation results confirm the practical applicability and performance of the approach.