Journal of Bionic Engineering ›› 2023, Vol. 20 ›› Issue (4): 1828-1851.doi: 10.1007/s42235-023-00336-y
A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System
Serdar Ekinci1; Davut Izci1; Laith Abualigah2,3,4,5,6,7; Raed Abu Zitar8
- 1 Department of Computer Engineering, Batman University, 72100 Batman, Turkey 2 Center for Engineering Application and Technology Solutions, Ho Chi Minh City Open University, Ho Chi Minh, Vietnam 3 Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan 4 Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq 25113, Jordan 5 Faculty of Information Technology, Middle East University, Amman 11831, Jordan 6 Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan 7 School of Computer Sciences, Universiti Sains Malaysia, 11800 Penang, Pulau Pinang, Malaysia 8 Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi, United Arab Emirates
A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System
Serdar Ekinci1; Davut Izci1; Laith Abualigah2,3,4,5,6,7; Raed Abu Zitar8
- 1 Department of Computer Engineering, Batman University, 72100 Batman, Turkey 2 Center for Engineering Application and Technology Solutions, Ho Chi Minh City Open University, Ho Chi Minh, Vietnam 3 Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan 4 Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq 25113, Jordan 5 Faculty of Information Technology, Middle East University, Amman 11831, Jordan 6 Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan 7 School of Computer Sciences, Universiti Sains Malaysia, 11800 Penang, Pulau Pinang, Malaysia 8 Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi, United Arab Emirates
摘要： In this work, we propose a real proportional-integral-derivative plus second-order derivative (PIDD2) controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient operation. In this regard, this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective system. We construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning mechanism. We also propose a simple yet effective objective function to increase the performance of the proposed algorithm (CmOBL-AO) to adjust the real PIDD2 controller's parameters effectively. We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm, gravitational search algorithm, African vultures optimization, and the Aquila Optimizer using well-known unimodal, multimodal benchmark functions. CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm. For the vehicle cruise control system, we confirm the more excellent performance of the proposed method against particle swarm, gray wolf, salp swarm, and original Aquila optimizers using statistical, Wilcoxon signed-rank, time response, robustness, and disturbance rejection analyses. We also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider perspective. The excellent performance of the proposed method is also illustrated through different quality indicators and different operating speeds. Lastly, we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic cases. We show the CmOBL-AO-based real PIDD2 controller as the most efficient method to control a vehicle cruise control system.