NDLI: A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics

Content Provider	IEEE Xplore Digital Library
Author	Chow, T.W.S. Yong Fang
Copyright Year	1982
Abstract	In this paper, the authors present a real-time learning control scheme for unknown nonlinear dynamical systems using recurrent neural networks (RNNs). Two RNNs, based on the same network architecture, are utilized in the learning control system. One is used to approximate the nonlinear system, and the other is used to mimic the desired system response output. The learning rule is achieved by combining the two RNNs to form the neural network control system. A generalized real-time iterative learning algorithm is developed and used to train the RNNs. The algorithm is derived by means of two-dimensional (2-D) system theory that is different from the conventional algorithms that employ the steepest optimization to minimize a cost function. This paper shows that an RNN using the real-time iterative learning algorithm can approximate any trajectory tracking to a very high degree of accuracy. The proposed learning control scheme is applied to numerical problems, and simulation results are included. The results are very promising, and this paper suggests that the 2-D system theory-based RNN learning algorithm provides a new dimension in real-time neural control systems.
Sponsorship	IEEE Industrial Electronics Society
Starting Page	151
Ending Page	161
Page Count	11
File Size	382818
File Format	PDF
ISSN	02780046
Volume Number	45
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Recurrent neural networks Iterative algorithms Control systems Real time systems Nonlinear control systems Nonlinear dynamical systems Nonlinear systems Neural networks Two dimensional displays Cost function
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Nonlinear dynamical systems control using a new RNN temporal learning strategy

A real-time learning control approach for nonlinear continuous-time system using recurrent neural networks

Identification of Nonlinear Dynamic System Using a Novel Recurrent Wavelet Neural Network Based on the Pipelined Architecture

Model Predictive Control of Nonlinear Systems With Unmodeled Dynamics Based on Feedforward and Recurrent Neural Networks

Model Predictive Control of Unknown Nonlinear Dynamical Systems Based on Recurrent Neural Networks

Dynamic Recurrent Neural Networks for Control of Unknown Nonlinear Systems

Approximation of dynamical time-variant systems by continuous-time recurrent neural networks

Recurrent-Neural-Network-Based Multivariable Adaptive Control for a Class of Nonlinear Dynamic Systems With Time-Varying Delay

Prediction of chaotic time series based on the recurrent predictor neural network

A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics

Similar Documents

Nonlinear dynamical systems control using a new RNN temporal learning strategy

A real-time learning control approach for nonlinear continuous-time system using recurrent neural networks

Identification of Nonlinear Dynamic System Using a Novel Recurrent Wavelet Neural Network Based on the Pipelined Architecture

Model Predictive Control of Nonlinear Systems With Unmodeled Dynamics Based on Feedforward and Recurrent Neural Networks

Model Predictive Control of Unknown Nonlinear Dynamical Systems Based on Recurrent Neural Networks

Dynamic Recurrent Neural Networks for Control of Unknown Nonlinear Systems

Approximation of dynamical time-variant systems by continuous-time recurrent neural networks

Recurrent-Neural-Network-Based Multivariable Adaptive Control for a Class of Nonlinear Dynamic Systems With Time-Varying Delay

Prediction of chaotic time series based on the recurrent predictor neural network

A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics