Inverse fuzzy-process-model based direct adaptive control

J. Abonyi, Hans Andersen, Lajos Nagy, F. Szeifert

Research output: Contribution to journalArticle

44 Citations (Scopus)

Abstract

This paper proposes a direct adaptive fuzzy-model-based control algorithm. The controller is based on an inverse semilinguistic fuzzy process model, identified and adapted via input-matching technique. For the adaptation of the fuzzy model a general learning rule has been developed employing gradient-descent algorithm. The on-line learning ability of the fuzzy model allows the controller to be used in applications, where the knowledge to control the process does not exist or the process is subject to changes in its dynamic characteristics. To demonstrate the applicability of the method, a realistic simulation experiments were performed for a non-linear liquid level process. The proposed direct adaptive fuzzy logic controller is shown to be capable of handling non-linear and time-varying systems dynamics, providing good overall system performance.

Original languageEnglish
Pages (from-to)119-132
Number of pages14
JournalMathematics and Computers in Simulation
Volume51
Issue number1-2
Publication statusPublished - Dec 22 1999

Fingerprint

Fuzzy Model
Adaptive Control
Process Model
Model-based
Controllers
Controller
Model-based Control
Descent Algorithm
Rule Learning
Time-varying Systems
Fuzzy Logic Controller
Time varying systems
Gradient Algorithm
Gradient Descent
Dynamic Characteristics
Fuzzy Control
System Dynamics
Fuzzy logic
Simulation Experiment
Control Algorithm

Keywords

  • Adaptive control
  • Fuzzy control
  • Fuzzy modelling
  • Non-linear dynamical systems

ASJC Scopus subject areas

  • Information Systems and Management
  • Control and Systems Engineering
  • Applied Mathematics
  • Computational Mathematics
  • Modelling and Simulation

Cite this

Inverse fuzzy-process-model based direct adaptive control. / Abonyi, J.; Andersen, Hans; Nagy, Lajos; Szeifert, F.

In: Mathematics and Computers in Simulation, Vol. 51, No. 1-2, 22.12.1999, p. 119-132.

Research output: Contribution to journalArticle

@article{d15bda8bdf2e4352b30796e19e8615fb,
title = "Inverse fuzzy-process-model based direct adaptive control",
abstract = "This paper proposes a direct adaptive fuzzy-model-based control algorithm. The controller is based on an inverse semilinguistic fuzzy process model, identified and adapted via input-matching technique. For the adaptation of the fuzzy model a general learning rule has been developed employing gradient-descent algorithm. The on-line learning ability of the fuzzy model allows the controller to be used in applications, where the knowledge to control the process does not exist or the process is subject to changes in its dynamic characteristics. To demonstrate the applicability of the method, a realistic simulation experiments were performed for a non-linear liquid level process. The proposed direct adaptive fuzzy logic controller is shown to be capable of handling non-linear and time-varying systems dynamics, providing good overall system performance.",
keywords = "Adaptive control, Fuzzy control, Fuzzy modelling, Non-linear dynamical systems",
author = "J. Abonyi and Hans Andersen and Lajos Nagy and F. Szeifert",
year = "1999",
month = "12",
day = "22",
language = "English",
volume = "51",
pages = "119--132",
journal = "Mathematics and Computers in Simulation",
issn = "0378-4754",
publisher = "Elsevier",
number = "1-2",

}

TY - JOUR

T1 - Inverse fuzzy-process-model based direct adaptive control

AU - Abonyi, J.

AU - Andersen, Hans

AU - Nagy, Lajos

AU - Szeifert, F.

PY - 1999/12/22

Y1 - 1999/12/22

N2 - This paper proposes a direct adaptive fuzzy-model-based control algorithm. The controller is based on an inverse semilinguistic fuzzy process model, identified and adapted via input-matching technique. For the adaptation of the fuzzy model a general learning rule has been developed employing gradient-descent algorithm. The on-line learning ability of the fuzzy model allows the controller to be used in applications, where the knowledge to control the process does not exist or the process is subject to changes in its dynamic characteristics. To demonstrate the applicability of the method, a realistic simulation experiments were performed for a non-linear liquid level process. The proposed direct adaptive fuzzy logic controller is shown to be capable of handling non-linear and time-varying systems dynamics, providing good overall system performance.

AB - This paper proposes a direct adaptive fuzzy-model-based control algorithm. The controller is based on an inverse semilinguistic fuzzy process model, identified and adapted via input-matching technique. For the adaptation of the fuzzy model a general learning rule has been developed employing gradient-descent algorithm. The on-line learning ability of the fuzzy model allows the controller to be used in applications, where the knowledge to control the process does not exist or the process is subject to changes in its dynamic characteristics. To demonstrate the applicability of the method, a realistic simulation experiments were performed for a non-linear liquid level process. The proposed direct adaptive fuzzy logic controller is shown to be capable of handling non-linear and time-varying systems dynamics, providing good overall system performance.

KW - Adaptive control

KW - Fuzzy control

KW - Fuzzy modelling

KW - Non-linear dynamical systems

UR - http://www.scopus.com/inward/record.url?scp=0038257993&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0038257993&partnerID=8YFLogxK

M3 - Article

VL - 51

SP - 119

EP - 132

JO - Mathematics and Computers in Simulation

JF - Mathematics and Computers in Simulation

SN - 0378-4754

IS - 1-2

ER -