A Longitudinal control for an autonomous vehicle using modified particle swarm optimization method

  • Authors

    • Ghaidaa Hadi Salih Elias university of kerbala
    2024-04-05
    https://doi.org/10.14419/q6bgfv04
  • Modified Particle Swarm Optimization Algorithm; Longitudinal Dynamics Vehicle; PSO Algorithm; Cost Function.

  • Abstract

    The control system is a very important part of the efficiency and safety of autonomous vehicles. This paper proposes the use of Particle Swarm Optimization (PSO) and Modified PSO (MPSO) algorithms to optimize Proportional Integral Derivative (PID) controller coefficients for a longitudinal dynamics vehicle system. The objective is to enhance system performance, measured by metrics such as maximum Overshoot (OS), Steady-State Error (SSE), Settling Time (t_s) (2%), and Rise Time 〖(t〗_r). The MPSO algorithm, when combined with the PID controller, demonstrates a 2.5% improvement over the traditional PSO algorithm. The study contributes by showcasing the effectiveness of MPSO in fine-tuning PID controllers for superior control of longitudinal vehicle dynamics, as evidenced by the optimized response specifications.

  • References

    1. Y. Kebbati, N. Aït-Oufroukh, V. Vigneron, and D. Ichalal, “Coordinated PSO-PID based longitudinal control with LPV-MPC based lateral control for autonomous vehicles,” in European Control Conference (ECC), 2022, pp. 518–523, https://doi.org/10.23919/ECC55457.2022.9838192.
    2. B. Paden, M. Čáp, S. Z. Yong, D. Yershov, and E. Frazzoli, “A survey of motion planning and control techniques for self-driving urban vehicles,” IEEE Trans. Intell. Veh., vol. 1, no. 1, pp. 33–55, 2016, https://doi.org/10.1109/TIV.2016.2578706.
    3. G. Hadi and A. A. A. Al-moadhen, “Optimization of Path Tracking of Self-Acting Mobile Robotic System,” University of Kerbala, 2022.
    4. G. H. S. Elias, A. Al-Moadhen, and H. Kamil, “Optimizing the PID controller to control the longitudinal motion of autonomous vehicles,” in AIP Conference Proceedings, 2023, vol. 2591, no. 1, p. 040045, https://doi.org/10.1063/5.0120396.
    5. S. Azam, F. Munir, and M. Jeon, “Dynamic Control System Design for Autonomous Car,” in International Conference on Vehicle Technology and Intelligent Transport Systems, 2020, pp. 456–463, https://doi.org/10.5220/0009392904560463.
    6. R. M. De Santis, “A novel PID configuration for speed and position control,” J. Dyn. Syst. Meas. Control, vol. 116, no. 3, pp. 542–549, 1994, https://doi.org/10.1115/1.2899250.
    7. S. Chen and H. Chen, “MPC-based path tracking with PID speed control for autonomous vehicles,” in IOP Conference Series: Materials Science and Engineering, 2020, vol. 892, no. 1, p. 012034, https://doi.org/10.1088/1757-899X/892/1/012034.
    8. M. Marcano, J. A. Matute, R. Lattarulo, E. Martí, and J. Pérez, “Low Speed Longitudinal Control Algorithms for Automated Vehicles in Simulation and Real Platforms,” Complexity, p. 12, 2018, https://doi.org/10.1155/2018/7615123.
    9. H. Kim, D. Kim, I. Shu, and K. Yi, “Time-varying parameter adaptive vehicle speed control,” IEEE Trans. Veh. Technol., vol. 65, no. 2, pp. 581–588, 2016, https://doi.org/10.1109/TVT.2015.2402756.
    10. S. Li, K. Li, R. Rajamani, and J. Wang, “Model predictive multi-objective vehicular adaptive cruise control,” IEEE Trans. Control Syst. Technol., vol. 19, no. 3, pp. 556–566, 2011, https://doi.org/10.1109/TCST.2010.2049203.
    11. J. E. Naranjo, C. Gonzàlez, R. García, and T. De Pedro, “ACC+Stop&Go maneuvers with throttle and brake fuzzy control,” IEEE Trans. Intell. Transp. Syst., vol. 7, no. 2, pp. 213–224, 2006, https://doi.org/10.1109/TITS.2006.874723.
    12. Y. Li and L. He, “Counterbalancing Speed Control for Hydrostatic Drive Heavy Vehicle under Long Down-Slope,” IEEE/ASME Trans. Mechatronics, vol. 20, no. 4, pp. 1533–1542, 2015, https://doi.org/10.1109/TMECH.2014.2385700.
    13. F. A. Ma’ani and Y. Y. Nazaruddin, “Optimization of Longitudinal Control of an Autonomous Vehicle using Flower Pollination Algorithm based on Data-driven Approach,” Int. J. Sustain. Transp. Technol., vol. 3, no. 2, pp. 58–65, 2020, https://doi.org/10.31427/IJSTT.2020.3.2.4.
    14. S. Allou and Y. Zennir, “A Comparative Study of PID-PSO and Fuzzy Controller for Path Tracking Control of Autonomous Ground Vehicles,” in 15th International Conference on Informatics in Control, Automation and Robotics, 2018, pp. 306–314, https://doi.org/10.5220/0006910903060314.
    15. M. Karami, A. R. Tavakolpour-Saleh, and A. Norouzi, “Optimal Nonlinear PID Control of a Micro-Robot Equipped with Vibratory Actuator Using Ant Colony Algorithm: Simulation and Experiment,” J. Intell. Robot. Syst., vol. 99, no. 3–4, pp. 773–796, 2020, https://doi.org/10.1007/s10846-020-01165-5.
    16. Y. Kebbati, N. Ait-oufroukh, V. Vigneron, D. Ichalal, and D. Gruyer, “Optimized self-adaptive PID speed control for autonomous vehicles To cite this version : HAL Id : hal-03442081 Optimized self-adaptive PID speed control for autonomous vehicles,” in International Conference on Automation and Computing (ICAC), 2021, pp. 1–6, https://doi.org/10.23919/ICAC50006.2021.9594131.
    17. R. Rajamani, “Longitudinal Vehicle Dynamics,” in Vehicle Dynamics and Control, 2nd Editio., Springer, Boston, MA, 2012, pp. 87–111. https://doi.org/10.1007/978-1-4614-1433-9_4.
    18. K. Ogata, Modern Control Engineering, Fourth edi. New Jmey: Prentiffi Hall, 2002.
    19. G. Hadi, S. Elias, A. Al-moadhen, and H. Kamil, “Lateral Control of an Autonomous Vehicle Based on Salp Swarm Algorithm,” 2023, vol. 030043, no. March, https://doi.org/10.1063/5.0120403.
    20. H. Liu, X. W. Zhang, and L. P. Tu, “A modified particle swarm optimization using adaptive strategy,” Expert Syst. Appl., vol. 152, p. 113353, 2020, https://doi.org/10.1016/j.eswa.2020.113353.

  • Downloads

  • How to Cite

    Hadi Salih Elias , G. (2024). A Longitudinal control for an autonomous vehicle using modified particle swarm optimization method. International Journal of Engineering & Technology, 13(1), 123-131. https://doi.org/10.14419/q6bgfv04