Open Access

ARTICLE

Modified Elite Opposition-Based Artificial Hummingbird Algorithm for Designing FOPID Controlled Cruise Control System

Laith Abualigah^{1,2,3,4,5,6,*}, Serdar Ekinci⁷, Davut Izci^7,8, Raed Abu Zitar⁹

1 Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq, 25113, Jordan
2 Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon
3 Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan
4 Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan
5 School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, Malaysia
6 School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya, 27500, Malaysia
7 Department of Computer Engineering, Batman University, Batman, 72100, Turkey
8 MEU Research Unit, Middle East University, Amman, Jordan
9 Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi, United Arab Emirates

* Corresponding Author: Laith Abualigah. Email:

Intelligent Automation & Soft Computing 2023, 38(2), 169-183. https://doi.org/10.32604/iasc.2023.040291

Received 13 March 2023; Accepted 22 May 2023; Issue published 05 February 2024

Abstract

Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability. This study proposes a novel approach for designing a fractional order proportional-integral-derivative (FOPID) controller that utilizes a modified elite opposition-based artificial hummingbird algorithm (m-AHA) for optimal parameter tuning. Our approach outperforms existing optimization techniques on benchmark functions, and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision. Our study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving experience while ensuring safety and reliability. We highlight the significance of our findings by demonstrating how our approach can improve the performance, safety, and reliability of autonomous vehicles. This study’s contributions are particularly relevant in the context of the growing demand for autonomous vehicles and the need for advanced control techniques to ensure their safe operation. Our research provides a promising avenue for further research and development in this area.

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