Optimal fractional order PID controller design for fractional order systems by Stochastic Multi Parameter Divergence Optimization Method with different random distribution functions

A. Ates; B. B. Alagoz; Y.Q. Chen; C. Yeroglu; S. Hassan Hosseinnia

doi:10.1109/ICCMA46720.2019.8988599

Optimal fractional order PID controller design for fractional order systems by Stochastic Multi Parameter Divergence Optimization Method with different random distribution functions

A. Ates, B. B. Alagoz, Y.Q. Chen, C. Yeroglu, S. Hassan Hosseinnia

Mechatronic Systems Design

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

2 Citations (Scopus)

Abstract

This paper modifies Stochastic Multi Parameter Divergence Optimization Method (SMDO) by using some types of random distribution functions in order to show effects of different random distribution functions on optimization performance. SMDO is a parameter wise random search algorithm in random walk class. A prominent feature of SMDO method lies in using random number with standard uniform distribution while diverging a parameter of solution point in backward and forward directions to reach an optimal solution. SMDO method benefits from the success of random backward and forward divergences. This study investigates effects of four types of random distribution functions on performance of SMDO algorithm for controller tuning problem. These distributions are Chi-Square Distribution (CSD), Rayleigh Distribution (RD), Log Normal Distribution (LND) and Uniform random (UD) distribution. To illustrate effects of these random distribution functions, SMDO is employed to fractional order PID (FOPID) controller tuning problems for fractional order model (FOM) and results obtained for different distribution functions are demonstrated.

Original language	English
Title of host publication	Proceedings of the IEEE 7th International Conference on Control, Mechatronics and Automation, ICCMA 2019
Place of Publication	Piscataway, NJ, USA
Publisher	IEEE
Pages	9-14
ISBN (Electronic)	978-1-7281-3787-2
DOIs	https://doi.org/10.1109/ICCMA46720.2019.8988599
Publication status	Published - 2019
Event	7th IEEE International Conference on Control, Mechatronics and Automation, ICCMA 2019 - Delft, Netherlands Duration: 6 Nov 2019 → 8 Nov 2019

Conference

Conference	7th IEEE International Conference on Control, Mechatronics and Automation, ICCMA 2019
Country/Territory	Netherlands
City	Delft
Period	6/11/19 → 8/11/19

Keywords

controller design
distribution functions
SMDO

Access to Document

10.1109/ICCMA46720.2019.8988599

Cite this

Ates, A., Alagoz, B. B., Chen, Y. Q., Yeroglu, C., & Hosseinnia, S. H. (2019). Optimal fractional order PID controller design for fractional order systems by Stochastic Multi Parameter Divergence Optimization Method with different random distribution functions. In Proceedings of the IEEE 7th International Conference on Control, Mechatronics and Automation, ICCMA 2019 (pp. 9-14). IEEE. https://doi.org/10.1109/ICCMA46720.2019.8988599

Ates, A. ; Alagoz, B. B. ; Chen, Y.Q. et al. / Optimal fractional order PID controller design for fractional order systems by Stochastic Multi Parameter Divergence Optimization Method with different random distribution functions. Proceedings of the IEEE 7th International Conference on Control, Mechatronics and Automation, ICCMA 2019. Piscataway, NJ, USA : IEEE, 2019. pp. 9-14

@inproceedings{eddaa80f6c1041938edf51b6fb84627d,

title = "Optimal fractional order PID controller design for fractional order systems by Stochastic Multi Parameter Divergence Optimization Method with different random distribution functions",

abstract = "This paper modifies Stochastic Multi Parameter Divergence Optimization Method (SMDO) by using some types of random distribution functions in order to show effects of different random distribution functions on optimization performance. SMDO is a parameter wise random search algorithm in random walk class. A prominent feature of SMDO method lies in using random number with standard uniform distribution while diverging a parameter of solution point in backward and forward directions to reach an optimal solution. SMDO method benefits from the success of random backward and forward divergences. This study investigates effects of four types of random distribution functions on performance of SMDO algorithm for controller tuning problem. These distributions are Chi-Square Distribution (CSD), Rayleigh Distribution (RD), Log Normal Distribution (LND) and Uniform random (UD) distribution. To illustrate effects of these random distribution functions, SMDO is employed to fractional order PID (FOPID) controller tuning problems for fractional order model (FOM) and results obtained for different distribution functions are demonstrated.",

keywords = "controller design, distribution functions, SMDO",

author = "A. Ates and Alagoz, {B. B.} and Y.Q. Chen and C. Yeroglu and Hosseinnia, {S. Hassan}",

year = "2019",

doi = "10.1109/ICCMA46720.2019.8988599",

language = "English",

pages = "9--14",

booktitle = "Proceedings of the IEEE 7th International Conference on Control, Mechatronics and Automation, ICCMA 2019",

publisher = "IEEE",

address = "United States",

note = "7th IEEE International Conference on Control, Mechatronics and Automation, ICCMA 2019 ; Conference date: 06-11-2019 Through 08-11-2019",

}

Ates, A, Alagoz, BB, Chen, YQ, Yeroglu, C & Hosseinnia, SH 2019, Optimal fractional order PID controller design for fractional order systems by Stochastic Multi Parameter Divergence Optimization Method with different random distribution functions. in Proceedings of the IEEE 7th International Conference on Control, Mechatronics and Automation, ICCMA 2019. IEEE, Piscataway, NJ, USA, pp. 9-14, 7th IEEE International Conference on Control, Mechatronics and Automation, ICCMA 2019, Delft, Netherlands, 6/11/19. https://doi.org/10.1109/ICCMA46720.2019.8988599

Optimal fractional order PID controller design for fractional order systems by Stochastic Multi Parameter Divergence Optimization Method with different random distribution functions. / Ates, A.; Alagoz, B. B.; Chen, Y.Q. et al.
Proceedings of the IEEE 7th International Conference on Control, Mechatronics and Automation, ICCMA 2019. Piscataway, NJ, USA: IEEE, 2019. p. 9-14.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Optimal fractional order PID controller design for fractional order systems by Stochastic Multi Parameter Divergence Optimization Method with different random distribution functions

AU - Ates, A.

AU - Alagoz, B. B.

AU - Chen, Y.Q.

AU - Yeroglu, C.

AU - Hosseinnia, S. Hassan

PY - 2019

Y1 - 2019

N2 - This paper modifies Stochastic Multi Parameter Divergence Optimization Method (SMDO) by using some types of random distribution functions in order to show effects of different random distribution functions on optimization performance. SMDO is a parameter wise random search algorithm in random walk class. A prominent feature of SMDO method lies in using random number with standard uniform distribution while diverging a parameter of solution point in backward and forward directions to reach an optimal solution. SMDO method benefits from the success of random backward and forward divergences. This study investigates effects of four types of random distribution functions on performance of SMDO algorithm for controller tuning problem. These distributions are Chi-Square Distribution (CSD), Rayleigh Distribution (RD), Log Normal Distribution (LND) and Uniform random (UD) distribution. To illustrate effects of these random distribution functions, SMDO is employed to fractional order PID (FOPID) controller tuning problems for fractional order model (FOM) and results obtained for different distribution functions are demonstrated.

AB - This paper modifies Stochastic Multi Parameter Divergence Optimization Method (SMDO) by using some types of random distribution functions in order to show effects of different random distribution functions on optimization performance. SMDO is a parameter wise random search algorithm in random walk class. A prominent feature of SMDO method lies in using random number with standard uniform distribution while diverging a parameter of solution point in backward and forward directions to reach an optimal solution. SMDO method benefits from the success of random backward and forward divergences. This study investigates effects of four types of random distribution functions on performance of SMDO algorithm for controller tuning problem. These distributions are Chi-Square Distribution (CSD), Rayleigh Distribution (RD), Log Normal Distribution (LND) and Uniform random (UD) distribution. To illustrate effects of these random distribution functions, SMDO is employed to fractional order PID (FOPID) controller tuning problems for fractional order model (FOM) and results obtained for different distribution functions are demonstrated.

KW - controller design

KW - distribution functions

KW - SMDO

UR - http://www.scopus.com/inward/record.url?scp=85081047847&partnerID=8YFLogxK

U2 - 10.1109/ICCMA46720.2019.8988599

DO - 10.1109/ICCMA46720.2019.8988599

M3 - Conference contribution

SP - 9

EP - 14

BT - Proceedings of the IEEE 7th International Conference on Control, Mechatronics and Automation, ICCMA 2019

PB - IEEE

CY - Piscataway, NJ, USA

T2 - 7th IEEE International Conference on Control, Mechatronics and Automation, ICCMA 2019

Y2 - 6 November 2019 through 8 November 2019

ER -

Ates A, Alagoz BB, Chen YQ, Yeroglu C, Hosseinnia SH. Optimal fractional order PID controller design for fractional order systems by Stochastic Multi Parameter Divergence Optimization Method with different random distribution functions. In Proceedings of the IEEE 7th International Conference on Control, Mechatronics and Automation, ICCMA 2019. Piscataway, NJ, USA: IEEE. 2019. p. 9-14 doi: 10.1109/ICCMA46720.2019.8988599

Optimal fractional order PID controller design for fractional order systems by Stochastic Multi Parameter Divergence Optimization Method with different random distribution functions

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this