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 controller design for unknown nonlinear systems using backstepping.

Y Zhang1, P Y Peng, Z P Jiang

  • 1Numerical Technologies Inc., San Jose, CA 95134-2134, USA. yzhang@numeritech.com

IEEE Transactions on Neural Networks
|February 6, 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

Infestation and distribution of chigger mites on Brown rat (Rattus norvegicus) in Yunnan Province, Southwest China.

Tropical biomedicine·2021
Same author

Effect of mild acid treatment on the survival, enteropathogenicity, and protein production in vibrio parahaemolyticus.

Infection and immunity·1998
Same author

The effects of K+ channel blockers on the spontaneous electrical and contractile activity in the proximal renal pelvis of the guinea pig.

The Journal of urology·1996
Same author

Interferon-gamma inhibits HIV-induced invasiveness of monocytes.

Journal of leukocyte biology·1995
Same author

Phosphorylation of Raf by ceramide-activated protein kinase.

Nature·1995
Same author

Cyclin D3 is essential for megakaryocytopoiesis.

Blood·1995
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

This study introduces a novel neural controller for nonlinear systems, enhancing stability analysis in adaptive control. The controller integrates neural networks with backstepping techniques for improved online performance and systematic design.

Area of Science:

  • Adaptive Control Theory
  • Neural Network Applications in Control
  • Nonlinear System Analysis

Background:

  • Existing neural controllers often lack systematic stability analysis.
  • Neural network design choices (structure, weights, training speed) are frequently arbitrary.
  • Understanding closed-loop system stability is crucial for reliable neural control.

Purpose of the Study:

  • To propose a neural controller for unknown, minimum phase, feedback linearizable nonlinear systems.
  • To address the inadequate stability analysis in current neural control schemes.
  • To integrate adaptive control principles with neural networks and backstepping.

Main Methods:

  • Utilized a backstepping design technique combined with a linearly parameterized neural network.

Related Experiment Videos

  • Shifted complex mechanical design aspects from offline to online implementation.
  • Employed Lyapunov analysis to demonstrate semiglobal stability properties.

    • Main Results:

    • The proposed neural controller achieves semiglobal stability for a class of nonlinear systems.
    • The controller can be trained online for different plants with the same relative degree.
    • Performance properties comparable to standard backstepping controllers are preserved.

    Conclusions:

    • The developed neural controller offers a systematic approach to stability analysis in adaptive control.
    • Online training capability enhances flexibility for controlling various nonlinear systems.
    • This method provides a viable alternative to traditional backstepping controllers with improved stability insights.