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U-model Based Adaptive IMC for Nonlinear Dynamic Plants.

Butt, Naveed LU and Shafiq, Muhammad (2005) 10th IEEE conference on emerging technologies and factory automation. 1. p.955-959
Abstract
A novel technique, involving U-model based IMC (Internal Model Control), is proposed for the adaptive control of nonlinear dynamic plants. The proposed scheme combines the robustness of the IMC and the ability of Neural Networks to identify arbitrary nonlinear functions, with the control-oriented nature of the U-model to achieve adaptive tracking of stable nonlinear plants. The proposed structure has a more general appeal than many other schemes involving polynomial NARMAX (Nonlinear Autoregressive Moving Average with Exogenous inputs) model and the Hammerstein model, etc. Additionally, the control law is shown to be more simplistic in nature. The effectiveness of the proposed scheme is demonstrated with the help of simulations for the... (More)
A novel technique, involving U-model based IMC (Internal Model Control), is proposed for the adaptive control of nonlinear dynamic plants. The proposed scheme combines the robustness of the IMC and the ability of Neural Networks to identify arbitrary nonlinear functions, with the control-oriented nature of the U-model to achieve adaptive tracking of stable nonlinear plants. The proposed structure has a more general appeal than many other schemes involving polynomial NARMAX (Nonlinear Autoregressive Moving Average with Exogenous inputs) model and the Hammerstein model, etc. Additionally, the control law is shown to be more simplistic in nature. The effectiveness of the proposed scheme is demonstrated with the help of simulations for the adaptive control of the Hammerstein model. © 2005 IEEE (Less)
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author
and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Internal Model Control (IMC), Nonlinear functions, Hammerstein models, Adaptive control systems, Computer simulation, Industrial plants, Mathematical models, Neural networks, Polynomials
host publication
IEEE Symposium on Emerging Technologies and Factory Automation, ETFA
volume
1
pages
955 - 959
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
10th IEEE conference on emerging technologies and factory automation.
conference location
Catania, Italy
conference dates
2005-09-19 - 2005-09-22
external identifiers
  • scopus:33847300140
ISBN
078039402X
language
English
LU publication?
no
id
10186317-d973-4ede-bb64-558ba8f719e4 (old id 1238200)
date added to LUP
2016-04-04 11:43:03
date last changed
2022-04-16 03:53:40
@inproceedings{10186317-d973-4ede-bb64-558ba8f719e4,
  abstract     = {{A novel technique, involving U-model based IMC (Internal Model Control), is proposed for the adaptive control of nonlinear dynamic plants. The proposed scheme combines the robustness of the IMC and the ability of Neural Networks to identify arbitrary nonlinear functions, with the control-oriented nature of the U-model to achieve adaptive tracking of stable nonlinear plants. The proposed structure has a more general appeal than many other schemes involving polynomial NARMAX (Nonlinear Autoregressive Moving Average with Exogenous inputs) model and the Hammerstein model, etc. Additionally, the control law is shown to be more simplistic in nature. The effectiveness of the proposed scheme is demonstrated with the help of simulations for the adaptive control of the Hammerstein model. © 2005 IEEE}},
  author       = {{Butt, Naveed and Shafiq, Muhammad}},
  booktitle    = {{IEEE Symposium on Emerging Technologies and Factory Automation, ETFA}},
  isbn         = {{078039402X}},
  keywords     = {{Internal Model Control (IMC); Nonlinear functions; Hammerstein models; Adaptive control systems; Computer simulation; Industrial plants; Mathematical models; Neural networks; Polynomials}},
  language     = {{eng}},
  pages        = {{955--959}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{U-model Based Adaptive IMC for Nonlinear Dynamic Plants.}},
  volume       = {{1}},
  year         = {{2005}},
}