Integral Reinforcement Learning Control for a Class of High-Order Multivariable Nonlinear Dynamics With Unknown Control Coefficients

This paper develops an integral reinforcement learning (IRL) controller for a class of high-order multivariable nonlinear systems with unknown control coefficients (UCCs). A new long-term performance index is first presented, and then the critic neural network (NN) and the action NN are presented to...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.86223-86229
Main Author: Wang, Qingling
Format: Article
Language:English
Subjects:
Artificial neural networks
Closed loop systems
Controllers
Dynamical systems
Feedback control
integral reinforcement learning
Integrals
Learning
Learning (artificial intelligence)
Manganese
MIMO communication
Multivariable control
Neural networks
Nonlinear control
Nonlinear dynamics
Nonlinear systems
nonsquare multivariable systems
Nussbaum-type functions
Performance analysis
Performance indices
Stability analysis
unknown control coefficients
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper develops an integral reinforcement learning (IRL) controller for a class of high-order multivariable nonlinear systems with unknown control coefficients (UCCs). A new long-term performance index is first presented, and then the critic neural network (NN) and the action NN are presented to estimate the unobtainable long-term performance index and the unknown drift of systems, respectively. By combining the critic and action NNs with Nussbaum-type functions, the IRL controllers for high-order, nonsquare multivariable systems are proposed to cope with the problem of UCCs. The analysis are given to illustrate that the stability of the closed-loop system can be obtained, and the signals of the closed-loop systems are semiglobally uniformly ultimately bounded (UUB). Finally, one simulation example is provided to show the effectiveness of the proposed IRL controllers.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2993265