Skip to main content
SpringerLink
Log in

A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System

  • Research Article
  • Published:
Journal of Bionic Engineering Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

Data Availability

Data are available from the authors upon reasonable request.

References

  1. Badrinath, J., Anita, J. P., & Sudheesh, P. (2017). Lateral prediction in adaptive cruise control using adaptive particle filter. International Conference on Advances in Computing, Communications and Informatics (ICACCI). https://doi.org/10.1109/ICACCI.2017.8126090

    Article  Google Scholar 

  2. Pradhan, R., Majhi, S. K., Pradhan, J. K., & Pati, B. B. (2017). Performance evaluation of PID controller for an automobile cruise control system using ant lion optimizer. Engineering Journal, 21(5), 347–361. https://doi.org/10.4186/ej.2017.21.5.347

    Article  Google Scholar 

  3. Hu, L., Zhong, Y., Hao, W., Moghimi, B., Huang, J., Zhang, X., & Du, R. (2018). Optimal route algorithm considering traffic light and energy consumption. IEEE Access, 6, 59695–59704. https://doi.org/10.1109/ACCESS.2018.2871843

    Article  Google Scholar 

  4. Lin, Y.-C., & Nguyen, H. L. T. (2020). Adaptive neuro-fuzzy predictor-based control for cooperative adaptive cruise control system. IEEE Transactions on Intelligent Transportation Systems, 21(3), 1054–1063. https://doi.org/10.1109/TITS.2019.2901498

    Article  Google Scholar 

  5. Gulzar, M. M., Sharif, B., Sibtain, D., Akbar, L., & Akhtar, A. (2019). Modelling and controller design of automotive cruise control system using hybrid model predictive controller. International Conference on Emerging Technologies (ICET). https://doi.org/10.1109/ICET48972.2019.8994444

    Article  Google Scholar 

  6. Blondin, M. J., & Trovão, J. P. (2019). Soft-computing techniques for cruise controller tuning for an off-road electric vehicle. IET Electrical Systems in Transportation, 9(4), 196–205. https://doi.org/10.1049/iet-est.2019.0008

    Article  Google Scholar 

  7. Osman, K., Rahmat, M. F., & Ahmad, M. A. (2009). Modelling and controller design for a cruise control system. International Colloquium on Signal Processing and Its Application. https://doi.org/10.1109/CSPA.2009.5069228

    Article  Google Scholar 

  8. Wu, W., Zou, D., Ou, J., & Hu, L. (2020). Adaptive cruise control strategy design with optimized active braking control algorithm. Mathematical Problems in Engineering, 2020, 1–10. https://doi.org/10.1155/2020/8382734

    Article  Google Scholar 

  9. Huang, J., Chen, Y., Peng, X., Hu, L., & Cao, D. (2020). Study on the driving style adaptive vehicle longitudinal control strategy. IEEE/CAA Journal of Automatica Sinica, 7(4), 1107–1115. https://doi.org/10.1109/JAS.2020.1003261

    Article  Google Scholar 

  10. Onieva, E., Godoy, J., Villagrá, J., Milanés, V., & Pérez, J. (2013). On-line learning of a fuzzy controller for a precise vehicle cruise control system. Expert Systems with Applications, 40(4), 1046–1053. https://doi.org/10.1016/j.eswa.2012.08.036

    Article  Google Scholar 

  11. Rout, M. K., Sain, D., Swain, S. K., & Mishra, S. K. (2016). PID controller design for cruise control system using genetic algorithm. International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). https://doi.org/10.1109/ICEEOT.2016.7755502

    Article  Google Scholar 

  12. Pradhan, R., Majhi, S. K., Pradhan, J. K., & Pati, B. B. (2018). Antlion optimizer tuned PID controller based on Bode ideal transfer function for automobile cruise control system. Journal of Industrial Information Integration, 9, 45–52. https://doi.org/10.1016/j.jii.2018.01.002

    Article  Google Scholar 

  13. Pradhan, R., & Pati, B. B. (2018). Optimal FOPID controller for an automobile cruise control system. International Conference on Recent Innovations in Electrical, Electronics and Communication Engineering (ICRIEEECE). https://doi.org/10.1109/ICRIEECE44171.2018.9008957

    Article  Google Scholar 

  14. Izci, D., & Ekinci, S. (2022). A novel hybrid ASO-NM algorithm and its application to automobile cruise control system. In G. Mathur, M. Bundele, L. Mahendra, & M. Paprzycki (Eds.), International conference on artificial intelligence: advances and applications (1st ed., pp. 333–343). Springer. https://doi.org/10.1007/978-981-16-6332-1_29

    Chapter  Google Scholar 

  15. Izci, D., & Ekinci, S. (2021). An efficient FOPID controller design for vehicle cruise control system using HHO algorithm. International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). https://doi.org/10.1109/HORA52670.2021.9461336

    Article  Google Scholar 

  16. Sahib, M. A. (2015). A novel optimal PID plus second order derivative controller for AVR system. Engineering Science and Technology, an International Journal, 18(2), 194–206. https://doi.org/10.1016/j.jestch.2014.11.006

    Article  Google Scholar 

  17. Micev, M., Calasan, M., & Radulovic, M. (2021). Optimal design of real PID plus second-order derivative controller for AVR system. International Conference on Information Technology (IT). https://doi.org/10.1109/IT51528.2021.9390145

    Article  Google Scholar 

  18. Ekinci, S., Izci, D., & Kayri, M. (2022). An effective controller design approach for magnetic levitation system using novel improved manta ray foraging optimization. Arabian Journal for Science and Engineering, 47(8), 9673–9694. https://doi.org/10.1007/s13369-021-06321-z

    Article  Google Scholar 

  19. Połap, D., & Woz´niak, M. (2017). Polar bear optimization algorithm: Meta-heuristic with fast population movement and dynamic birth and death Mechanism. Symmetry, 9(10), 203. https://doi.org/10.3390/sym9100203

    Article  Google Scholar 

  20. Połap, D., & Woźniak, M. (2021). Red fox optimization algorithm. Expert Systems with Applications, 166, 114107. https://doi.org/10.1016/j.eswa.2020.114107

    Article  Google Scholar 

  21. Izci, D., Ekinci, S., Zeynelgil, H. L., & Hedley, J. (2022). Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems. Transactions of the Institute of Measurement and Control, 44(2), 435–456. https://doi.org/10.1177/01423312211037967

    Article  Google Scholar 

  22. Bian, Q., Ma, J., Zhao, X., Wang, J., & Meng, D. (2020). Control parameter optimization for automobile cruise control system via improved differential evolution algorithm. CICTP, 2020, 2236–2248. https://doi.org/10.1061/9780784483053.188

    Article  Google Scholar 

  23. Dawood, S. Y., et al. (2018). Comparison of PID, GA and fuzzy logic controllers for cruise control system. International Journal of Computing and Digital Systems, 7(5), 311–319. https://doi.org/10.12785/ijcds/070505

    Article  Google Scholar 

  24. Izci, D., Ekinci, S., Kayri, M., & Eker, E. (2021). A novel enhanced metaheuristic algorithm for automobile cruise control system. Electrica, 21(3), 283–297. https://doi.org/10.5152/electrica.2021.21016

    Article  Google Scholar 

  25. Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A. A., & Gandomi, A. H. (2021). Aquila optimizer: a novel meta-heuristic optimization algorithm. Computers & Industrial Engineering, 157, 107250. https://doi.org/10.1016/j.cie.2021.107250

    Article  Google Scholar 

  26. Luo, J., Chen, H., Heidari, A. A., Xu, Y., Zhang, Q., & Li, C. (2019). Multi-strategy boosted mutative whale-inspired optimization approaches. Applied Mathematical Modelling, 73, 109–123. https://doi.org/10.1016/j.apm.2019.03.046

    Article  MathSciNet  MATH  Google Scholar 

  27. Tizhoosh, H. R. (2005). Opposition-based learning: a new scheme for machine intelligence. International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06). https://doi.org/10.1109/CIMCA.2005.1631345

    Article  Google Scholar 

  28. Gaing, Z.-L. (2004). A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Transactions on Energy Conversion, 19(2), 384–391. https://doi.org/10.1109/TEC.2003.821821

    Article  Google Scholar 

  29. Izci, D., Ekinci, S., Eker, E., & Kayri, M. (2022). A novel modified opposition-based hunger games search algorithm to design fractional order proportional-integral-derivative controller for magnetic ball suspension system. Advanced Control for Applications, 4(1), e96. https://doi.org/10.1002/adc2.96

    Article  Google Scholar 

  30. Storn, R., & Price, K. (1997). Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359. https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  31. Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248. https://doi.org/10.1016/j.ins.2009.03.004

    Article  MATH  Google Scholar 

  32. Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021). African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems. Computers and Industrial Engineering, 158, 107408. https://doi.org/10.1016/j.cie.2021.107408

    Article  Google Scholar 

  33. Izci, D., Ekinci, S., Kayri, M., & Eker, E. (2022). A novel improved arithmetic optimization algorithm for optimal design of PID controlled and Bode’s ideal transfer function based automobile cruise control system. Evolving Systems, 13(3), 453–468. https://doi.org/10.1007/s12530-021-09402-4

    Article  Google Scholar 

  34. Gharehchopogh, F. S., Nadimi-Shahraki, M. H., Barshandeh, S., Abdollahzadeh, B., & Zamani, H. (2022). CQFFA: A chaotic quasi-oppositional farmland fertility algorithm for solving engineering optimization problems. Journal of Bionic Engineering. https://doi.org/10.1007/s42235-022-00255-4

    Article  Google Scholar 

  35. Gupta, S., & Deep, K. (2019). A hybrid self-adaptive sine cosine algorithm with opposition based learning. Expert Systems with Applications, 119, 210–230. https://doi.org/10.1016/j.eswa.2018.10.050

    Article  Google Scholar 

  36. Ewees, A. A., Abd Elaziz, M., & Oliva, D. (2021). A new multi-objective optimization algorithm combined with opposition-based learning. Expert Systems with Applications, 165, 113844. https://doi.org/10.1016/j.eswa.2020.113844

    Article  Google Scholar 

  37. Shekhawat, S., & Saxena, A. (2020). Development and applications of an intelligent crow search algorithm based on opposition based learning. ISA Transactions, 99, 210–230. https://doi.org/10.1016/j.isatra.2019.09.004

    Article  Google Scholar 

  38. Gupta, S., Deep, K., Heidari, A. A., Moayedi, H., & Wang, M. (2020). Opposition-based learning Harris hawks optimization with advanced transition rules: principles and analysis. Expert Systems with Applications, 158, 113510. https://doi.org/10.1016/j.eswa.2020.113510

    Article  Google Scholar 

  39. Long, W., Jiao, J., Liang, X., Cai, S., & Xu, M. (2019). A random opposition-based learning grey wolf optimizer. IEEE Access, 7, 113810–113825. https://doi.org/10.1109/ACCESS.2019.2934994

    Article  Google Scholar 

  40. Sun, L., Chen, S., Xu, J., & Tian, Y. (2019). Improved monarch butterfly optimization algorithm based on opposition-based learning and random local perturbation. Complexity, 2019, 1–20. https://doi.org/10.1155/2019/4182148

    Article  Google Scholar 

  41. Izci, D., Ekinci, S., & Hekimoğlu, B. (2022). A novel modified Lévy flight distribution algorithm to tune proportional, integral, derivative and acceleration controller on buck converter system. Transactions of the Institute of Measurement and Control, 44(2), 393–409. https://doi.org/10.1177/01423312211036591

    Article  Google Scholar 

  42. Liang, X., Cai, Z., Wang, M., Zhao, X., Chen, H., & Li, C. (2020). Chaotic oppositional sine–cosine method for solving global optimization problems. Engineering with Computers. https://doi.org/10.1007/s00366-020-01083-y

    Article  Google Scholar 

  43. Çelik, E. (2020). A powerful variant of symbiotic organisms search algorithm for global optimization. Engineering Applications of Artificial Intelligence, 87, 103294. https://doi.org/10.1016/j.engappai.2019.103294

    Article  Google Scholar 

  44. Emami, H., & Alipour, M. M. (2021). Chaotic local search-based levy flight distribution algorithm for optimizing ONU placement in fiber-wireless access network. Optical Fiber Technology, 67, 102733. https://doi.org/10.1016/j.yofte.2021.102733

    Article  Google Scholar 

  45. Chen, H., Zhang, Q., Luo, J., Xu, Y., & Zhang, X. (2020). An enhanced bacterial foraging optimization and its application for training kernel extreme learning machine. Applied Soft Computing, 86, 105884. https://doi.org/10.1016/j.asoc.2019.105884

    Article  Google Scholar 

  46. Gao, S., Yu, Y., Wang, Y., Wang, J., Cheng, J., & Zhou, M. (2021). Chaotic local search-based differential evolution algorithms for optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(6), 3954–3967. https://doi.org/10.1109/TSMC.2019.2956121

    Article  Google Scholar 

  47. Xu, Y., Chen, H., Heidari, A. A., Luo, J., Zhang, Q., Zhao, X., & Li, C. (2019). An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks. Expert Systems with Applications, 129, 135–155. https://doi.org/10.1016/j.eswa.2019.03.043

    Article  Google Scholar 

  48. Li, C., Li, J., Chen, H., Jin, M., & Ren, H. (2021). Enhanced Harris hawks optimization with multi-strategy for global optimization tasks. Expert Systems with Applications, 185, 115499. https://doi.org/10.1016/j.eswa.2021.115499

    Article  Google Scholar 

  49. Hussien, A. G., & Amin, M. (2021). A self-adaptive Harris Hawks optimization algorithm with opposition-based learning and chaotic local search strategy for global optimization and feature selection. International Journal of Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-021-01326-4

    Article  Google Scholar 

  50. Huang, H., Feng, X., Heidari, A. A., Xu, Y., Wang, M., Liang, G., Chen, H., & Cai, X. (2020). Rationalized sine cosine optimization with efficient searching patterns. IEEE Access, 8, 61471–61490. https://doi.org/10.1109/ACCESS.2020.2983451

    Article  Google Scholar 

  51. Chen, H., Wang, M., & Zhao, X. (2020). A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems. Applied Mathematics and Computation, 369, 124872. https://doi.org/10.1016/j.amc.2019.124872

    Article  MathSciNet  MATH  Google Scholar 

  52. Li, S., & Wu, L. (2021). An improved salp swarm algorithm for locating critical slip surface of slopes. Arabian Journal of Geosciences, 14(5), 359. https://doi.org/10.1007/s12517-021-06687-2

    Article  Google Scholar 

  53. Lewis, P. H., & Houghton, Y. (1997). Basic control systems engineering. Prentice Hall.

    Google Scholar 

  54. Frank, A. A., Liu, S. J., & Liang, S. C. (1989). Longitudinal control concepts for automated automobiles and trucks operating on a cooperative highway. SAE Transactions, 98, 1308–1315.

    Google Scholar 

  55. Izci, D. (2021). Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder-Mead algorithm. Transactions of the Institute of Measurement and Control, 43(14), 3195–3211. https://doi.org/10.1177/01423312211019633

    Article  Google Scholar 

  56. Kumar, M., & Hote, Y. V. (2021). Real-time performance analysis of PIDD2 controller for nonlinear twin rotor TITO aerodynamical system. Journal of Intelligent & Robotic Systems, 101(3), 55. https://doi.org/10.1007/s10846-021-01322-4

    Article  Google Scholar 

  57. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICCN’95-International Conference on Neural Networks. https://doi.org/10.1109/ICNN.1995.488968

    Article  Google Scholar 

  58. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  59. Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002

    Article  Google Scholar 

  60. Izci, D., Ekinci, S., Demiroren, A., & Hedley, J. (2020). HHO algorithm based PID controller design for aircraft pitch angle control system. International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). https://doi.org/10.1109/HORA49412.2020.9152897

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Batman University, 72100, Batman, Turkey

    Serdar Ekinci & Davut Izci

  2. Center for Engineering Application and Technology Solutions, Ho Chi Minh City Open University, Ho Chi Minh, Vietnam

    Laith Abualigah

  3. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  4. Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  5. Faculty of Information Technology, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  6. Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan

    Laith Abualigah

  7. School of Computer Sciences, Universiti Sains Malaysia, 11800, Penang, Pulau Pinang, Malaysia

    Laith Abualigah

  8. Sorbonne Center of Artificial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi, United Arab Emirates

    Raed Abu Zitar

Authors
  1. Serdar Ekinci
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Davut Izci
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laith Abualigah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Raed Abu Zitar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Laith Abualigah.

Ethics declarations

Conflict of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ekinci, S., Izci, D., Abualigah, L. et al. A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System. J Bionic Eng 20, 1828–1851 (2023). https://doi.org/10.1007/s42235-023-00336-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42235-023-00336-y

Keywords

Search

Navigation