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

Nonlinear adaptive trajectory tracking using dynamic neural networks.

A S Poznyak1, W Yu, E N Sanchez

  • 1Departamento de Control Automatico, CINVESTAV-IPN, Mexico D.F., 07360, Mexico.

IEEE Transactions on Neural Networks
|February 7, 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

ΔNp73 status in peritoneal and ovarian dissemination of appendicular adenocarcinoids (goblet cells).

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico·2019
Same author

Osteoimmunology in orthodontic tooth movement.

Oral diseases·2014
Same author

Percutaneous balloon compression (PBC) of trigeminal ganglion for recurrent trigeminal neuralgia after microvascular decompression (MVD).

Irish journal of medical science·2014
Same author

Exome sequencing of pleuropulmonary blastoma reveals frequent biallelic loss of TP53 and two hits in DICER1 resulting in retention of 5p-derived miRNA hairpin loop sequences.

Oncogene·2014
Same author

Comparison of the incidence between tuberculosis and nontuberculous mycobacterial disease after gastrectomy.

Infection·2014
Same author

Efficacy and safety of gabapentin 1800 mg treatment for post-herpetic neuralgia: a meta-analysis of randomized controlled trials.

Journal of clinical pharmacy and therapeutics·2014
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 dynamic neural networks for adaptive nonlinear identification and trajectory tracking. We establish theorems bounding identification and tracking errors, validated with relay and chaotic Duffing systems.

Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Nonlinear Dynamics

Background:

  • Adaptive nonlinear systems require robust identification and precise trajectory tracking.
  • Dynamic neural networks offer a promising approach for complex system control.
  • Stability and error bounds are critical for validating control strategies.

Purpose of the Study:

  • To develop and analyze adaptive nonlinear identification and trajectory tracking using dynamic neural networks.
  • To establish theoretical bounds for identification and tracking errors.
  • To demonstrate the practical application of the proposed methods on complex systems.

Main Methods:

  • Lyapunov-like analysis for determining stability conditions of identification error.

Related Experiment Videos

  • Local optimal controller for analyzing trajectory tracking error.
  • Utilizing algebraic and differential Riccati equations for error analysis.
  • Formulating two original theorems to bound identification and tracking errors.

    • Main Results:

    • Established stability conditions for the identification error using Lyapunov-like analysis.
    • Derived bounds for both the identification error and the trajectory tracking error through two novel theorems.
    • Successfully illustrated the effectiveness of the proposed methods on a second-order relay system and the chaotic Duffing equation.

    Conclusions:

    • The proposed dynamic neural network approach provides effective adaptive nonlinear identification and trajectory tracking.
    • The derived error bounds offer theoretical guarantees for system performance.
    • The validated examples highlight the method's applicability to diverse and challenging nonlinear systems.