MFAC parameter optimization based on improved sparrow search algorithm

Tongfu Xu; Xiuying Li

doi:10.1117/12.2660146

9 December 2022 MFAC parameter optimization based on improved sparrow search algorithm

Tongfu Xu, Xiuying Li

Proceedings Volume 12492, International Workshop on Automation, Control, and Communication Engineering (IWACCE 2022); 1249207 (2022) https://doi.org/10.1117/12.2660146
Event: International Workshop on Automation, Control, and Communication Engineering (IWACCE 2022), 2022, Wuhan, China

Abstract

Model-free adaptive control (MFAC) is based on data and does not depend on the mathematical model of the controlled object. It is simple in structure and easy to implement. At present, there are few methods to determine the parameters of MFAC controller, which brings considerable inconvenience to the research and application. Therefore, an improved sparrow search algorithm (ISSA) is proposed to optimize the parameters of MFAC. The ISSA improves its performance by introducing the piecewise chaotic map operator, and finally realizes the automatic optimization of MFAC. The results show that the ISSA has better searching speed and precision. After using optimized parameters for control, the overshoot is greatly reduced, and the oscillation phenomenon when the desired output changes or the external disturbance occurs is effectively overcome, which makes the system more robust.

Citation Download Citation

Tongfu Xu and Xiuying Li "MFAC parameter optimization based on improved sparrow search algorithm", Proc. SPIE 12492, International Workshop on Automation, Control, and Communication Engineering (IWACCE 2022), 1249207 (9 December 2022); https://doi.org/10.1117/12.2660146

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available