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

Reinforcement learning neural-network-based controller for nonlinear discrete-time systems with input constraints.

Pingan He1, S Jagannathan

  • 1Department of Electrical and Computer Engineering, University of Missouri-Rolla, Rolla, MO 65409, USA. ph8p5@umr.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|April 10, 2007
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

Microbiologically influenced corrosion dynamics and technological innovations in monitoring and control.

Journal of microbiological methods·2026
Same author

Microbiome-transcriptome-histology triad enhances survival risk stratification in multiple cancers.

Computational biology and chemistry·2025
Same author

A novel number-theoretic sampling method for neural network solutions of partial differential equations.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Online lifelong optimal tracking control of uncertain nonlinear continuous-time strict-feedback systems using deep neural networks.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Identification of a novel phage depolymerase against ST11 K64 carbapenem-resistant <i>Klebsiella pneumoniae</i> and its therapeutic potential.

Journal of bacteriology·2025
Same author

Molecular Epidemiology and Horizontal Transfer Mechanism of <i>optrA</i>-Carrying Linezolid-Resistant <i>Enterococcus faecalis</i>.

Polish journal of microbiology·2024
Same journal

Strategic Ability Updating in Concurrent Games by Coalitional Commitment.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2015
Same journal

Meta-Analysis of the First Facial Expression Recognition Challenge.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Adjustable model-based fusion method for multispectral and panchromatic images.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Face Feature Weighted Fusion Based on Fuzzy Membership Degree for Video Face Recognition.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

A New Adaptive Fast Cellular Automaton Neighborhood Detection and Rule Identification Algorithm.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Human-arm-and-hand-dynamic model with variability analyses for a stylus-based haptic interface.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
See all related articles

This study introduces a novel adaptive-critic neural network (NN) controller for nonlinear systems with actuator constraints. The controller ensures desired tracking performance despite saturation nonlinearities and NN approximation errors.

Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Nonlinear Dynamics

Background:

  • Actuator constraints, specifically saturation nonlinearity, pose significant challenges in controlling complex nonlinear systems.
  • Existing control schemes often struggle to handle multiple nonlinearities simultaneously, limiting their applicability.
  • Adaptive-critic neural networks (NNs) offer a promising approach for robust control but require careful design for practical implementation.

Purpose of the Study:

  • To design a novel discrete-time adaptive-critic-based neural network (NN) controller for nonlinear systems with actuator constraints.
  • To ensure desired tracking performance by effectively managing saturation nonlinearities within the controller architecture.
  • To develop a controller capable of handling multiple nonlinearities and NN approximation errors without requiring offline training.

Related Experiment Videos

Main Methods:

  • A state-feedback adaptive-critic NN controller architecture employing two NNs: a critic NN for utility function approximation and an action NN for minimization.
  • Treatment of actuator saturation as a nonlinearity within the controller design.
  • Derivation of NN weight updates by minimizing quadratic performance indexes, utilizing a Lyapunov approach for stability analysis.

Main Results:

  • The proposed controller demonstrates uniformly ultimate boundedness of the closed-loop tracking error and weight estimates.
  • The controller effectively handles multiple nonlinearities, a significant advantage over existing methods.
  • The adaptive-critic NN controller does not require an offline training phase, allowing for zero or random weight initialization.

Conclusions:

  • The novel adaptive-critic NN controller provides robust tracking performance for nonlinear systems with actuator constraints.
  • The controller's ability to manage multiple nonlinearities and its online learning capability make it suitable for complex applications.
  • Simulation results validate the theoretical analysis and the effectiveness of the proposed control strategy.