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 Experiment Videos

Stable neural-network-based adaptive control for sampled-data nonlinear systems.

F Sun1, Z Sun, P Y Woo

  • 1Department of Computer Science and Technology, State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China.

IEEE Transactions on Neural Networks
|February 8, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Clinical Oncologist and NHS Commissioner Perspectives on Reirradiation for Recurrent Cancer.

Clinical oncology (Royal College of Radiologists (Great Britain))·2026
Same author

Effects of replacing porcine blood meal with rumen-protected lysine, while maintaining metabolizable methionine supply, on performance, nitrogen use efficiency, and nutrient partitioning in dairy cows.

Journal of dairy science·2026
Same author

Effects of a metabolizable protein supply on metabolic function of CD4<sup>+</sup> T lymphocytes in mid-lactation dairy cows.

Journal of dairy science·2026
Same author

[Treatment of peri-implant mucositis using an erythritol air-polishing or ultrasonic device: A randomized controlled trial].

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences·2026
Same author

[Construction of evidence graph of modifiable risk factors for diabetic nephropathy].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2025
Same author

[Interpretation of the 2025 "Guidelines for the diagnosis and treatment of congenital hypothyroidism"].

Zhonghua yi xue za zhi·2025
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

A new stable neural-network (NN)-based adaptive control method enhances control for nonlinear sampled-data systems. This approach integrates NNs with variable structure control, ensuring system stability and tracking error convergence for improved performance.

Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Nonlinear Dynamics

Background:

  • Controlling complex multi-input-multi-output (MIMO) sampled-data nonlinear systems with unknown dynamics presents significant challenges.
  • Existing adaptive control methods may struggle with inherent nonlinearities and uncertainties in such systems.

Purpose of the Study:

  • To develop a stable neural-network (NN)-based adaptive control approach for MIMO sampled-data nonlinear systems.
  • To integrate NN approximation capabilities with variable structure control (VSC) for robust performance.

Main Methods:

  • The proposed method combines a neural network (NN) approach with an adaptive implementation of variable structure control (VSC) with a sector.
  • VSC is utilized to maintain system states within the NN's operational region and provide supplementary control.

Related Experiment Videos

  • Stability and tracking error convergence are rigorously proven, and parameter tuning is discussed.

    • Main Results:

    • The adaptive control strategy ensures complete stability and convergence of the tracking error.
    • The asymptotic error is shown to depend on NN approximation errors and unmodeled dynamics frequency range.
    • Effectiveness demonstrated through simulation studies on a two-link manipulator.

    Conclusions:

    • The integrated NN and VSC approach offers a stable and effective solution for controlling complex nonlinear sampled-data systems.
    • The method provides robust performance by managing unknown nonlinearities and system uncertainties.
    • This technique holds promise for applications requiring precise control of dynamic systems.