Adaptive Quasi-Optimal Gains Tuning of PI-Fuzzy Controllers

Paulo José Carrilho de Sousa Gil; DEE Group Author; Luís Filipe Figueira de Brito Palma

doi:10.1109/INES.2012.6249867

Adaptive Quasi-Optimal Gains Tuning of PI-Fuzzy Controllers

Paulo José Carrilho de Sousa Gil, DEE Group Author, Luís Filipe Figueira de Brito Palma

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper proposes a new methodology for tuning the scaling factors, or gains, of fuzzy proportional-integral controllers, where the closed loop system performance is explicitly taken into account. The tuning problem is formulated as an online constrained non-linear optimization problem using a linear model structure to capture the system's dynamics. The model parameters are updated online using the most recent information collected from the plant. In the case of stringent sampling times the optimization routine is prematurely stopped before a local minimum is reached and the obtained gains subsequently provided to the controller. Experimental results from a testbed illustrate the relevance of the proposed tuning technique.

Original language	Unknown
Title of host publication	IEEE
Pages	399 - 404
ISBN (Electronic)	978-1-4673-2693-3
DOIs	https://doi.org/10.1109/INES.2012.6249867
Publication status	Published - 1 Jan 2012
Event	IEEE 16th International Conference on Intelligent Engineering Systems - Duration: 1 Jan 2012 → …

Conference

Conference	IEEE 16th International Conference on Intelligent Engineering Systems
Period	1/01/12 → …

Access to Document

10.1109/INES.2012.6249867

Cite this

@inproceedings{9bd050eb252a421b82b6fa338297a3dd,

title = "Adaptive Quasi-Optimal Gains Tuning of PI-Fuzzy Controllers",

abstract = "This paper proposes a new methodology for tuning the scaling factors, or gains, of fuzzy proportional-integral controllers, where the closed loop system performance is explicitly taken into account. The tuning problem is formulated as an online constrained non-linear optimization problem using a linear model structure to capture the system's dynamics. The model parameters are updated online using the most recent information collected from the plant. In the case of stringent sampling times the optimization routine is prematurely stopped before a local minimum is reached and the obtained gains subsequently provided to the controller. Experimental results from a testbed illustrate the relevance of the proposed tuning technique.",

keywords = "fuzzy control, PI control, nonlinear programming, control system synthesis, optimal control, adaptive control",

author = "Gil, {Paulo Jos{\'e} Carrilho de Sousa} and {DEE Group Author} and Palma, {Lu{\'i}s Filipe Figueira de Brito}",

year = "2012",

month = jan,

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doi = "10.1109/INES.2012.6249867",

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AU - Gil, Paulo José Carrilho de Sousa

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AU - Palma, Luís Filipe Figueira de Brito

PY - 2012/1/1

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N2 - This paper proposes a new methodology for tuning the scaling factors, or gains, of fuzzy proportional-integral controllers, where the closed loop system performance is explicitly taken into account. The tuning problem is formulated as an online constrained non-linear optimization problem using a linear model structure to capture the system's dynamics. The model parameters are updated online using the most recent information collected from the plant. In the case of stringent sampling times the optimization routine is prematurely stopped before a local minimum is reached and the obtained gains subsequently provided to the controller. Experimental results from a testbed illustrate the relevance of the proposed tuning technique.

AB - This paper proposes a new methodology for tuning the scaling factors, or gains, of fuzzy proportional-integral controllers, where the closed loop system performance is explicitly taken into account. The tuning problem is formulated as an online constrained non-linear optimization problem using a linear model structure to capture the system's dynamics. The model parameters are updated online using the most recent information collected from the plant. In the case of stringent sampling times the optimization routine is prematurely stopped before a local minimum is reached and the obtained gains subsequently provided to the controller. Experimental results from a testbed illustrate the relevance of the proposed tuning technique.

KW - fuzzy control

KW - PI control

KW - nonlinear programming

KW - control system synthesis

KW - optimal control

KW - adaptive control

U2 - 10.1109/INES.2012.6249867

DO - 10.1109/INES.2012.6249867

M3 - Conference contribution

SN - 978-1-4673-2694-0

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EP - 404

BT - IEEE

T2 - IEEE 16th International Conference on Intelligent Engineering Systems

Y2 - 1 January 2012

ER -