Implementation of fuzzy control for a nonlinear system - Conical level process

K. Ashutha, Eadala Sarath Yadav, Thirunavukkarasu Indiran, C. Shreesha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Fuzzy logic is the trending control strategy and ideal in its performance to the real world of control. It has been implemented in most of the control fields because of its expertise in fault tolerance, knowledge representation, nonlinearity, uncertainty, real time operation etc. In this paper mamdani type of fuzzy controller is considered for controlling nonlinear conical tank process. The identification of conical tank system is performed by two point method and fuzzy controller is implemented. The results of fuzzy controller is compared with AMIGO (Approximate M constrained Integral Gain Optimization) and conventional controllers for evaluating its performance indices like IAE, ITAE and ISE using MATLAB simulation environment and real time experimentation was also carried out. It is also observed that the fuzzy controllers perform better than that of AMIGO PI and conventional PI controller.

Original languageEnglish
Title of host publication2017 9th International Conference on Information Technology and Electrical Engineering, ICITEE 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
Volume2018-January
ISBN (Electronic)9781509064779
DOIs
Publication statusPublished - 08-01-2018
Event9th International Conference on Information Technology and Electrical Engineering, ICITEE 2017 - Phuket, Thailand
Duration: 12-10-201713-10-2017

Conference

Conference9th International Conference on Information Technology and Electrical Engineering, ICITEE 2017
CountryThailand
CityPhuket
Period12-10-1713-10-17

Fingerprint

Fuzzy Controller
Fuzzy control
Fuzzy Control
Nonlinear systems
Nonlinear Systems
Controllers
Matlab Simulation
PI Controller
Optimization
Knowledge Representation
Performance Index
Simulation Environment
Expertise
Fault Tolerance
Experimentation
Fuzzy Logic
Control Strategy
Knowledge representation
Nonlinearity
Fault tolerance

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this

Ashutha, K., Yadav, E. S., Indiran, T., & Shreesha, C. (2018). Implementation of fuzzy control for a nonlinear system - Conical level process. In 2017 9th International Conference on Information Technology and Electrical Engineering, ICITEE 2017 (Vol. 2018-January, pp. 1-4). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICITEED.2017.8250455
Ashutha, K. ; Yadav, Eadala Sarath ; Indiran, Thirunavukkarasu ; Shreesha, C. / Implementation of fuzzy control for a nonlinear system - Conical level process. 2017 9th International Conference on Information Technology and Electrical Engineering, ICITEE 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-4
@inproceedings{6ea12a2b4eb241438bba69505760320c,
title = "Implementation of fuzzy control for a nonlinear system - Conical level process",
abstract = "Fuzzy logic is the trending control strategy and ideal in its performance to the real world of control. It has been implemented in most of the control fields because of its expertise in fault tolerance, knowledge representation, nonlinearity, uncertainty, real time operation etc. In this paper mamdani type of fuzzy controller is considered for controlling nonlinear conical tank process. The identification of conical tank system is performed by two point method and fuzzy controller is implemented. The results of fuzzy controller is compared with AMIGO (Approximate M constrained Integral Gain Optimization) and conventional controllers for evaluating its performance indices like IAE, ITAE and ISE using MATLAB simulation environment and real time experimentation was also carried out. It is also observed that the fuzzy controllers perform better than that of AMIGO PI and conventional PI controller.",
author = "K. Ashutha and Yadav, {Eadala Sarath} and Thirunavukkarasu Indiran and C. Shreesha",
year = "2018",
month = "1",
day = "8",
doi = "10.1109/ICITEED.2017.8250455",
language = "English",
volume = "2018-January",
pages = "1--4",
booktitle = "2017 9th International Conference on Information Technology and Electrical Engineering, ICITEE 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

Ashutha, K, Yadav, ES, Indiran, T & Shreesha, C 2018, Implementation of fuzzy control for a nonlinear system - Conical level process. in 2017 9th International Conference on Information Technology and Electrical Engineering, ICITEE 2017. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 9th International Conference on Information Technology and Electrical Engineering, ICITEE 2017, Phuket, Thailand, 12-10-17. https://doi.org/10.1109/ICITEED.2017.8250455

Implementation of fuzzy control for a nonlinear system - Conical level process. / Ashutha, K.; Yadav, Eadala Sarath; Indiran, Thirunavukkarasu; Shreesha, C.

2017 9th International Conference on Information Technology and Electrical Engineering, ICITEE 2017. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-4.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Implementation of fuzzy control for a nonlinear system - Conical level process

AU - Ashutha, K.

AU - Yadav, Eadala Sarath

AU - Indiran, Thirunavukkarasu

AU - Shreesha, C.

PY - 2018/1/8

Y1 - 2018/1/8

N2 - Fuzzy logic is the trending control strategy and ideal in its performance to the real world of control. It has been implemented in most of the control fields because of its expertise in fault tolerance, knowledge representation, nonlinearity, uncertainty, real time operation etc. In this paper mamdani type of fuzzy controller is considered for controlling nonlinear conical tank process. The identification of conical tank system is performed by two point method and fuzzy controller is implemented. The results of fuzzy controller is compared with AMIGO (Approximate M constrained Integral Gain Optimization) and conventional controllers for evaluating its performance indices like IAE, ITAE and ISE using MATLAB simulation environment and real time experimentation was also carried out. It is also observed that the fuzzy controllers perform better than that of AMIGO PI and conventional PI controller.

AB - Fuzzy logic is the trending control strategy and ideal in its performance to the real world of control. It has been implemented in most of the control fields because of its expertise in fault tolerance, knowledge representation, nonlinearity, uncertainty, real time operation etc. In this paper mamdani type of fuzzy controller is considered for controlling nonlinear conical tank process. The identification of conical tank system is performed by two point method and fuzzy controller is implemented. The results of fuzzy controller is compared with AMIGO (Approximate M constrained Integral Gain Optimization) and conventional controllers for evaluating its performance indices like IAE, ITAE and ISE using MATLAB simulation environment and real time experimentation was also carried out. It is also observed that the fuzzy controllers perform better than that of AMIGO PI and conventional PI controller.

UR - http://www.scopus.com/inward/record.url?scp=85049579048&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049579048&partnerID=8YFLogxK

U2 - 10.1109/ICITEED.2017.8250455

DO - 10.1109/ICITEED.2017.8250455

M3 - Conference contribution

AN - SCOPUS:85049579048

VL - 2018-January

SP - 1

EP - 4

BT - 2017 9th International Conference on Information Technology and Electrical Engineering, ICITEE 2017

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Ashutha K, Yadav ES, Indiran T, Shreesha C. Implementation of fuzzy control for a nonlinear system - Conical level process. In 2017 9th International Conference on Information Technology and Electrical Engineering, ICITEE 2017. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-4 https://doi.org/10.1109/ICITEED.2017.8250455