Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Lecture Notes in Networks and Systems |
| Herausgeber (Verlag) | Springer |
| Seiten | 74-86 |
| Seitenumfang | 13 |
| ISBN (elektronisch) | 978-3-030-34983-7 |
| ISBN (Print) | 978-3-030-34982-0 |
| Publikationsstatus | Veröffentlicht - 30 Nov. 2019 |
| Extern publiziert | Ja |
Publikationsreihe
| Name | Lecture Notes in Networks and Systems |
|---|---|
| Band | 95 |
| ISSN (Print) | 2367-3370 |
| ISSN (elektronisch) | 2367-3389 |
Abstract
Conventional approaches to control systems still present a reasonable solution for a variety of different tasks in control engineering problems. Controllers based on the PID approach are used in a wide range of applications due to their easy handling, realization and set up, as well as their modest need of computational resources during the runtime. In order to heuristically find near-optimal parameters for the controller design, different approaches to tuning PID controllers have been developed. The Ziegler–Nichols methods are still commonly used despite that they have long been known, though modern methods, such as the T-Sum method, have also emerged. In this work, a comparison of the tuned PID controllers with a Mamdani-Fuzzy-Logic controller and an adaptive neural network controller is offered. A unified step response is used to classify the performance of controllers. It is shown that a PID control can work just as well as a fuzzy logic or neural network control for simple applications with time-invariant parameters or in applications where the parameters only change slightly and no strict constancy of the plant output is necessary.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Informatik (insg.)
- Signalverarbeitung
- Informatik (insg.)
- Computernetzwerke und -kommunikation
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Lecture Notes in Networks and Systems. Springer, 2019. S. 74-86 (Lecture Notes in Networks and Systems; Band 95).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review
}
TY - CHAP
T1 - A Comparison of Tuning Methods for PID-Controllers with Fuzzy and Neural Network Controllers
AU - Gross, Clemens
AU - Voelker, Hendrik
N1 - Publisher Copyright: © 2020, Springer Nature Switzerland AG.
PY - 2019/11/30
Y1 - 2019/11/30
N2 - Conventional approaches to control systems still present a reasonable solution for a variety of different tasks in control engineering problems. Controllers based on the PID approach are used in a wide range of applications due to their easy handling, realization and set up, as well as their modest need of computational resources during the runtime. In order to heuristically find near-optimal parameters for the controller design, different approaches to tuning PID controllers have been developed. The Ziegler–Nichols methods are still commonly used despite that they have long been known, though modern methods, such as the T-Sum method, have also emerged. In this work, a comparison of the tuned PID controllers with a Mamdani-Fuzzy-Logic controller and an adaptive neural network controller is offered. A unified step response is used to classify the performance of controllers. It is shown that a PID control can work just as well as a fuzzy logic or neural network control for simple applications with time-invariant parameters or in applications where the parameters only change slightly and no strict constancy of the plant output is necessary.
AB - Conventional approaches to control systems still present a reasonable solution for a variety of different tasks in control engineering problems. Controllers based on the PID approach are used in a wide range of applications due to their easy handling, realization and set up, as well as their modest need of computational resources during the runtime. In order to heuristically find near-optimal parameters for the controller design, different approaches to tuning PID controllers have been developed. The Ziegler–Nichols methods are still commonly used despite that they have long been known, though modern methods, such as the T-Sum method, have also emerged. In this work, a comparison of the tuned PID controllers with a Mamdani-Fuzzy-Logic controller and an adaptive neural network controller is offered. A unified step response is used to classify the performance of controllers. It is shown that a PID control can work just as well as a fuzzy logic or neural network control for simple applications with time-invariant parameters or in applications where the parameters only change slightly and no strict constancy of the plant output is necessary.
KW - Control engineering
KW - Fuzzy control
KW - Fuzzy logic
KW - Neural network control
KW - PID
KW - PID tuning
UR - http://www.scopus.com/inward/record.url?scp=85087573195&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-34983-7_8
DO - 10.1007/978-3-030-34983-7_8
M3 - Contribution to book/anthology
AN - SCOPUS:85087573195
SN - 978-3-030-34982-0
T3 - Lecture Notes in Networks and Systems
SP - 74
EP - 86
BT - Lecture Notes in Networks and Systems
PB - Springer
ER -