An approximate internal model-based neural control for unknown nonlinear discrete processes.

Full Abstract

An approximate internal model-based neural control (AIMNC) strategy is proposed for unknown nonaffine nonlinear discrete processes under disturbed environment. The proposed control strategy has some clear advantages in respect to existing neural internal model control methods. It can be used for open-loop unstable nonlinear processes or a class of systems with unstable zero dynamics. Based on a novel input-output approximation, the proposed neural control law can be derived directly and implemented straightforward for an unknown process. Only one neural network needs to be trained and control algorithm can be directly obtained from model identification without further training. The stability and robustness of a closed-loop system can be derived analytically. Extensive simulations demonstrate the superior performance of the proposed AIMNC strategy.

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Author information

Author/s: Li, Han-Xiong (HX); Deng, Hua (H);

Affiliation: Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong. mehxli(-atsign-)cityu.edu.hk

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council (IEEE Trans Neural Netw), published in United States. (Language: eng)

Reference: 2006-May; vol 17 (issue 3) : pp 659-70

Dates: Created 2006/05/25; Completed 2006/06/20; Revised 2006/11/15;

PMID: 16722170, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Artificial Intelligence
Computer Simulation
Feedback
Information Storage and Retrieval - methods

Models, Statistical
Neural Networks (Computer)
Nonlinear Dynamics
Pattern Recognition, Automated - methods
Systems Theory

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