MIMO U-model based control: Real-time tracking control and feedback analysis via small gain theorem

Ali Syed Saad Azhar; Muhammad Shafiq; Fouad M. Al-Sunni; Jamil M. Bakhashwain

MIMO U-model based control: Real-time tracking control and feedback analysis via small gain theorem

Ali Syed Saad Azhar^*, Muhammad Shafiq, Fouad M. Al-Sunni, Jamil M. Bakhashwain

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

In this paper, MIMO U-model based IMC is used for the tracking control of multivariable nonlinear systems. The algorithm is implemented in real-time on a 2DoF robot arm. The stability and convergence issues for the control-oriented U-model are also discussed. In order to guarantee stability and faster convergence speeds, bounds are suggested for the learning rate of adaptation algorithm that estimate the parameters of U-model. The adaptation algorithm is first associated with a feedback structure and then its stability is investigated using l₂ stability and small gain theorem. The paper also discusses about the robustness of adaptation algorithm in the presence of noise and suggests optimal choices for faster convergence speeds.

Original language	English
Pages (from-to)	610-619
Number of pages	10
Journal	WSEAS Transactions on Circuits and Systems
Volume	7
Issue number	7
Publication status	Published - 2008
Externally published	Yes

Keywords

Convergence speed
LMS
Learning rate
Small gain theorem
U-Model

ASJC Scopus subject areas

Electrical and Electronic Engineering

Cite this

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title = "MIMO U-model based control: Real-time tracking control and feedback analysis via small gain theorem",

abstract = "In this paper, MIMO U-model based IMC is used for the tracking control of multivariable nonlinear systems. The algorithm is implemented in real-time on a 2DoF robot arm. The stability and convergence issues for the control-oriented U-model are also discussed. In order to guarantee stability and faster convergence speeds, bounds are suggested for the learning rate of adaptation algorithm that estimate the parameters of U-model. The adaptation algorithm is first associated with a feedback structure and then its stability is investigated using l2 stability and small gain theorem. The paper also discusses about the robustness of adaptation algorithm in the presence of noise and suggests optimal choices for faster convergence speeds.",

keywords = "Convergence speed, LMS, Learning rate, Small gain theorem, U-Model",

author = "Azhar, {Ali Syed Saad} and Muhammad Shafiq and Al-Sunni, {Fouad M.} and Bakhashwain, {Jamil M.}",

year = "2008",

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T1 - MIMO U-model based control

T2 - Real-time tracking control and feedback analysis via small gain theorem

AU - Azhar, Ali Syed Saad

AU - Shafiq, Muhammad

AU - Al-Sunni, Fouad M.

AU - Bakhashwain, Jamil M.

PY - 2008

Y1 - 2008

N2 - In this paper, MIMO U-model based IMC is used for the tracking control of multivariable nonlinear systems. The algorithm is implemented in real-time on a 2DoF robot arm. The stability and convergence issues for the control-oriented U-model are also discussed. In order to guarantee stability and faster convergence speeds, bounds are suggested for the learning rate of adaptation algorithm that estimate the parameters of U-model. The adaptation algorithm is first associated with a feedback structure and then its stability is investigated using l2 stability and small gain theorem. The paper also discusses about the robustness of adaptation algorithm in the presence of noise and suggests optimal choices for faster convergence speeds.

AB - In this paper, MIMO U-model based IMC is used for the tracking control of multivariable nonlinear systems. The algorithm is implemented in real-time on a 2DoF robot arm. The stability and convergence issues for the control-oriented U-model are also discussed. In order to guarantee stability and faster convergence speeds, bounds are suggested for the learning rate of adaptation algorithm that estimate the parameters of U-model. The adaptation algorithm is first associated with a feedback structure and then its stability is investigated using l2 stability and small gain theorem. The paper also discusses about the robustness of adaptation algorithm in the presence of noise and suggests optimal choices for faster convergence speeds.

KW - Convergence speed

KW - LMS

KW - Learning rate

KW - Small gain theorem

KW - U-Model

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M3 - Article

AN - SCOPUS:59549083070

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JO - WSEAS Transactions on Circuits and Systems

JF - WSEAS Transactions on Circuits and Systems

IS - 7

ER -

MIMO U-model based control: Real-time tracking control and feedback analysis via small gain theorem

Abstract

Keywords

ASJC Scopus subject areas

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