Research on Model Predictive Control for Autonomous Car Assistance Systems Applications

International Journal of Computer Science and Engineering

© 2024 by SSRG - IJCSE Journal

Volume 11 Issue 7

Year of Publication : 2024

Authors : Nguyen Minh Huy, Nguyen Hoang Hiep, Bui Nhat Minh, Nguyen Ngoc Minh, Vo Thanh Ha

10.14445/23488387/IJCSE-V11I7P101

How to Cite?

Nguyen Minh Huy, Nguyen Hoang Hiep, Bui Nhat Minh, Nguyen Ngoc Minh, Vo Thanh Ha, "Research on Model Predictive Control for Autonomous Car Assistance Systems Applications," SSRG International Journal of Computer Science and Engineering , vol. 11,  no. 7, pp. 1-7, 2024. Crossref, https://doi.org/10.14445/23488387/IJCSE-V11I7P101

Abstract:

Autonomous vehicles are making significant progress in research and manufacturing, growing closer to becoming a reality. These vehicles function as complicated systems with many various parameters impacting their performance. To guarantee that autonomous cars can navigate properly, maintain a constant pace, and react adequately to their surroundings while keeping passengers secure and comfortable, it is necessary to apply sophisticated control systems to adapt to changing situations. This article describes a technique for driving autonomous cars utilizing Model Predictive Control (MPC) in conjunction with Simulink for model construction. By using script functions and critical constants like vehicle model parameters, controller design parameters, road conditions, and nearby vehicles, the MPC controller can handle various constraints such as speed limits, safe following distances, physical limits of the car, and obstacles that must be avoided. The usefulness of this strategy is proved via simulated situations at various speeds using MATLAB/Simulink. Results show that the MPC controller effectively manages the autonomous vehicle, ensuring safe and efficient navigation in different scenarios. As technology advances, incorporating advanced control systems like MPC will bring autonomous cars closer to widespread adoption.

Keywords:

MPC, Autonomous Car, PID, AIS, AUVs.

References:

[1] Adile Akpunar, and Serdar Iplikci, “Runge-Kutta Model Predictive Speed Control for Permanent Magnet Synchronous Motors,” Energies, vol. 13, no. 5, pp. 1-17, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Hussain Bassi, and Youssef Mobarak, “State-Space Modeling and Performance Analysis of Variable-Speed Wind Turbine Based on a Model Predictive Control Approach,” Engineering, Technology & Applied Science Research, vol. 7, no. 2, pp. 1436-1443, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Do Trong Tu, “Enhancing Road Holding and Vehicle Comfort for an Active Suspension System Utilizing Model Predictive Control and Deep Learning,” Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12931-12936, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Patrick Weyers, Alexander Barth, and Anton Kummert, “Driver State Monitoring with Hierarchical Classification,” 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, pp. 3239-3244, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Vo Thanh Ha, “Torque Control of an In-Wheel Axial Flux Permanent Magnet Synchronous Motor using a Fuzzy Logic Controller for Electric Vehicles,” Engineering, Technology & Applied Science Research, vol. 13, no. 2, pp. 10357-10362, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Andrea Minari, Aurelio Piazzi, and Alessandro Costalunga, “Polynomial Interpolation for Inversion-Based Control,” European Journal of Control, vol. 56, pp. 62-72, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Abdeslam Boularias et al., “Learning Qualitative Spatial Relations for Robotic Navigation,” Proceedings of 25th International Joint Conference on Artificial Intelligence (IJCAI-16), New York, USA, pp. 4130-4134, 2016.
[Google Scholar] [Publisher Link]
[8] Ahmed AbdElmoniem et al., “A Path-Tracking Algorithm Using Predictive Stanley Lateral Controller,” International Journal of Advanced Robotic Systems, vol. 17, no. 6, pp. 1-11, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Akinobu Goto, Takashi Fukushige, and Takeshi Kimura, “Real-Time Trajectory Planning for Autonomous Driving in Urban Area Based on Dynamic Programming,” Society of Automotive Engineers of Japan, vol. 52, no. 3, pp. 639-644, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Noor Hafizah Amer et al., “Modelling and Control Strategies in Path Tracking Control for Autonomous Ground Vehicles: A Review of State of the Art and Challenges,” Journal of Intelligent & Robotic Systems, vol. 86, pp. 225-254, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Anouer Bennajeh et al., “Bi-Level Decision-Making Modeling for an Autonomous Driver Agent: Application in the Car-Following Driving Behavior,” Applied Artificial Intelligence, vol. 33, no. 13, pp. 1157-1178, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Bandana Barman, Ria Kanjilal, and Anirban Mukhopadhyay, “Neuro-Fuzzy Controller Design to Navigate Unmanned Vehicle with Construction of Traffic Rules to Avoid Obstacles,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 24, no. 3, pp. 433-449, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Pedro Bautista-Camino et al., “Local Path Planning for Autonomous Vehicles Based on the Natural Behavior of the Biological Action-Perception Motion,” Energies, vol. 15, no. 5, pp. 1-23, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Boliang Yi et al., “Real Time Integrated Vehicle Dynamics Control and Trajectory Planning with MPC for Critical Maneuvers,” 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden, pp. 584-589, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Yong Chen et al., “Accurate and Efficient Approximation of Clothoids Using Bézier Curves for Path Planning,” IEEE Transactions on Robotics, vol. 33, no. 5, pp. 1242-1247, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Enrique Herrera-Viedma et al., “Special Issue on Intelligent Decision Support Systems Based on Soft Computing and Their Applications in Real-World Problems,” Applied Soft Computing, vol. 67, pp. 610-612, 2018.
[CrossRef] [Google Scholar] [Publisher Link]