Adaptive Controller Design for Unknown Systems Using Measured Data

Sofiane Khadraoui, Hazem Nounou, Mohamed Nounou, Aniruddha Datta, Shankar P. Bhattacharyya

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper presents a measurement-based adaptive control design approach for unknown systems working over a wide range of operating conditions. Traditional control design approaches usually require the availability of a mathematical model. However, it has been shown in many practical situations that, due to complex dynamics of physical systems, some simplifying assumptions are made for the derivation of mathematical models. Hence, controller design based on simplified models may result in degradation of the desired closed-loop performance. Data-based control design approaches can be viewed as an alternative approach to model-based methods. Most data-based control methods available in the literature aim to design controllers for unknown systems that operate only at a given operating point. However, the dynamical behavior of plants may change for different operating conditions, which makes the task of designing a controller that works over the entire range of operating conditions more challenging. In this paper, we address such a problem and propose to design adaptive controllers based on measured data. Such a proposed method is based on designing a set of measurement-based controllers validated at a finite set of pre-specified operating points. Then, the parameters of the adaptive controller are obtained by interpolating between the set of pre-designed controller parameters to derive a gain-scheduling controller. Moreover, low-order adaptive controllers can be designed by simply selecting the desired controller structure. The efficacy of the proposed approach is experimentally validated through a practical application to control a heating system operated over a large range of flow rate.

Original languageEnglish
Pages (from-to)1453-1466
Number of pages14
JournalAsian Journal of Control
Volume18
Issue number4
DOIs
Publication statusPublished - 1 Jul 2016

Fingerprint

Controllers
Mathematical models
Scheduling
Flow rate
Availability
Heating
Degradation

Keywords

  • frequency response
  • gain-scheduling techniques
  • low-order controller
  • Nonparametric model control design
  • operating conditions
  • unknown systems

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Adaptive Controller Design for Unknown Systems Using Measured Data. / Khadraoui, Sofiane; Nounou, Hazem; Nounou, Mohamed; Datta, Aniruddha; Bhattacharyya, Shankar P.

In: Asian Journal of Control, Vol. 18, No. 4, 01.07.2016, p. 1453-1466.

Research output: Contribution to journalArticle

Khadraoui, Sofiane ; Nounou, Hazem ; Nounou, Mohamed ; Datta, Aniruddha ; Bhattacharyya, Shankar P. / Adaptive Controller Design for Unknown Systems Using Measured Data. In: Asian Journal of Control. 2016 ; Vol. 18, No. 4. pp. 1453-1466.
@article{4b7a54cc9e50474493279f41d2215dcb,
title = "Adaptive Controller Design for Unknown Systems Using Measured Data",
abstract = "This paper presents a measurement-based adaptive control design approach for unknown systems working over a wide range of operating conditions. Traditional control design approaches usually require the availability of a mathematical model. However, it has been shown in many practical situations that, due to complex dynamics of physical systems, some simplifying assumptions are made for the derivation of mathematical models. Hence, controller design based on simplified models may result in degradation of the desired closed-loop performance. Data-based control design approaches can be viewed as an alternative approach to model-based methods. Most data-based control methods available in the literature aim to design controllers for unknown systems that operate only at a given operating point. However, the dynamical behavior of plants may change for different operating conditions, which makes the task of designing a controller that works over the entire range of operating conditions more challenging. In this paper, we address such a problem and propose to design adaptive controllers based on measured data. Such a proposed method is based on designing a set of measurement-based controllers validated at a finite set of pre-specified operating points. Then, the parameters of the adaptive controller are obtained by interpolating between the set of pre-designed controller parameters to derive a gain-scheduling controller. Moreover, low-order adaptive controllers can be designed by simply selecting the desired controller structure. The efficacy of the proposed approach is experimentally validated through a practical application to control a heating system operated over a large range of flow rate.",
keywords = "frequency response, gain-scheduling techniques, low-order controller, Nonparametric model control design, operating conditions, unknown systems",
author = "Sofiane Khadraoui and Hazem Nounou and Mohamed Nounou and Aniruddha Datta and Bhattacharyya, {Shankar P.}",
year = "2016",
month = "7",
day = "1",
doi = "10.1002/asjc.1227",
language = "English",
volume = "18",
pages = "1453--1466",
journal = "Asian Journal of Control",
issn = "1561-8625",
publisher = "National Taiwan University (IEEB)",
number = "4",

}

TY - JOUR

T1 - Adaptive Controller Design for Unknown Systems Using Measured Data

AU - Khadraoui, Sofiane

AU - Nounou, Hazem

AU - Nounou, Mohamed

AU - Datta, Aniruddha

AU - Bhattacharyya, Shankar P.

PY - 2016/7/1

Y1 - 2016/7/1

N2 - This paper presents a measurement-based adaptive control design approach for unknown systems working over a wide range of operating conditions. Traditional control design approaches usually require the availability of a mathematical model. However, it has been shown in many practical situations that, due to complex dynamics of physical systems, some simplifying assumptions are made for the derivation of mathematical models. Hence, controller design based on simplified models may result in degradation of the desired closed-loop performance. Data-based control design approaches can be viewed as an alternative approach to model-based methods. Most data-based control methods available in the literature aim to design controllers for unknown systems that operate only at a given operating point. However, the dynamical behavior of plants may change for different operating conditions, which makes the task of designing a controller that works over the entire range of operating conditions more challenging. In this paper, we address such a problem and propose to design adaptive controllers based on measured data. Such a proposed method is based on designing a set of measurement-based controllers validated at a finite set of pre-specified operating points. Then, the parameters of the adaptive controller are obtained by interpolating between the set of pre-designed controller parameters to derive a gain-scheduling controller. Moreover, low-order adaptive controllers can be designed by simply selecting the desired controller structure. The efficacy of the proposed approach is experimentally validated through a practical application to control a heating system operated over a large range of flow rate.

AB - This paper presents a measurement-based adaptive control design approach for unknown systems working over a wide range of operating conditions. Traditional control design approaches usually require the availability of a mathematical model. However, it has been shown in many practical situations that, due to complex dynamics of physical systems, some simplifying assumptions are made for the derivation of mathematical models. Hence, controller design based on simplified models may result in degradation of the desired closed-loop performance. Data-based control design approaches can be viewed as an alternative approach to model-based methods. Most data-based control methods available in the literature aim to design controllers for unknown systems that operate only at a given operating point. However, the dynamical behavior of plants may change for different operating conditions, which makes the task of designing a controller that works over the entire range of operating conditions more challenging. In this paper, we address such a problem and propose to design adaptive controllers based on measured data. Such a proposed method is based on designing a set of measurement-based controllers validated at a finite set of pre-specified operating points. Then, the parameters of the adaptive controller are obtained by interpolating between the set of pre-designed controller parameters to derive a gain-scheduling controller. Moreover, low-order adaptive controllers can be designed by simply selecting the desired controller structure. The efficacy of the proposed approach is experimentally validated through a practical application to control a heating system operated over a large range of flow rate.

KW - frequency response

KW - gain-scheduling techniques

KW - low-order controller

KW - Nonparametric model control design

KW - operating conditions

KW - unknown systems

UR - http://www.scopus.com/inward/record.url?scp=84949035314&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84949035314&partnerID=8YFLogxK

U2 - 10.1002/asjc.1227

DO - 10.1002/asjc.1227

M3 - Article

AN - SCOPUS:84949035314

VL - 18

SP - 1453

EP - 1466

JO - Asian Journal of Control

JF - Asian Journal of Control

SN - 1561-8625

IS - 4

ER -