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Higher-Order Neural Network Based Root-Solving Controller for Adaptive Tracking of Stable Nonlinear Plants

Butt, Naveed LU and Shafiq, Muhammad (2006) IEEE International Conference on Engineering of Intelligent Systems In IEEE international conference on engineering of intelligent systems. p.258-263
Abstract
The use of Intelligent control schemes in Nonlinear Model Based Control (NMBC) has gained widespread popularity. Neural Networks, in particular, have been used extensively to model the dynamics of nonlinear plants. However, in most cases, these models do not lend themselves to easy maneuvering for controller design. Therefore, a common need is being felt to develop intelligent control strategies that lead to computationally simple control laws. To achieve this objective, the present study combines the approximation power of Higher-Order Neural Networks (HONN) with the control-oriented nature of the recently developed U-model. By introducing the U-model equivalence of a Higher-Order Neural Unit (HONU), the control law synthesis part is... (More)
The use of Intelligent control schemes in Nonlinear Model Based Control (NMBC) has gained widespread popularity. Neural Networks, in particular, have been used extensively to model the dynamics of nonlinear plants. However, in most cases, these models do not lend themselves to easy maneuvering for controller design. Therefore, a common need is being felt to develop intelligent control strategies that lead to computationally simple control laws. To achieve this objective, the present study combines the approximation power of Higher-Order Neural Networks (HONN) with the control-oriented nature of the recently developed U-model. By introducing the U-model equivalence of a Higher-Order Neural Unit (HONU), the control law synthesis part is reduced to a simple polynomial root-solving procedure. The proposed scheme is based on the robust Internal Model Control (IMC) structure and is suitable for stable nonlinear plants with uncertain dynamics. The main feature of the proposed structure is its ability to capture higher-order nonlinear properties of the input pattern space while allowing the synthesis of a simple control law. The scheme is therefore expected to prove extremely useful in the area of nonlinear adaptive control. The effectiveness of the proposed scheme is demonstrated through application to various nonlinear models. (Less)
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author
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
INTERNAL-MODEL CONTROL, SYSTEMS, IDENTIFICATION
in
IEEE international conference on engineering of intelligent systems.
pages
258 - 263
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
IEEE International Conference on Engineering of Intelligent Systems
external identifiers
  • Scopus:40849135922
ISBN
978-1-4244-0456-8
language
English
LU publication?
no
id
93e38a47-3e40-4936-89eb-704081e0689a (old id 1232201)
date added to LUP
2008-10-20 15:53:32
date last changed
2017-02-19 04:30:13
@inproceedings{93e38a47-3e40-4936-89eb-704081e0689a,
  abstract     = {The use of Intelligent control schemes in Nonlinear Model Based Control (NMBC) has gained widespread popularity. Neural Networks, in particular, have been used extensively to model the dynamics of nonlinear plants. However, in most cases, these models do not lend themselves to easy maneuvering for controller design. Therefore, a common need is being felt to develop intelligent control strategies that lead to computationally simple control laws. To achieve this objective, the present study combines the approximation power of Higher-Order Neural Networks (HONN) with the control-oriented nature of the recently developed U-model. By introducing the U-model equivalence of a Higher-Order Neural Unit (HONU), the control law synthesis part is reduced to a simple polynomial root-solving procedure. The proposed scheme is based on the robust Internal Model Control (IMC) structure and is suitable for stable nonlinear plants with uncertain dynamics. The main feature of the proposed structure is its ability to capture higher-order nonlinear properties of the input pattern space while allowing the synthesis of a simple control law. The scheme is therefore expected to prove extremely useful in the area of nonlinear adaptive control. The effectiveness of the proposed scheme is demonstrated through application to various nonlinear models.},
  author       = {Butt, Naveed and Shafiq, Muhammad},
  booktitle    = {IEEE international conference on engineering of intelligent systems.},
  isbn         = {978-1-4244-0456-8},
  keyword      = {INTERNAL-MODEL CONTROL,SYSTEMS,IDENTIFICATION},
  language     = {eng},
  pages        = {258--263},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Higher-Order Neural Network Based Root-Solving Controller for Adaptive Tracking of Stable Nonlinear Plants},
  year         = {2006},
}