Research on Predictive Control for the Damping System of Autonomous Vehicles in the Public Transport on the Basis of Artificial Intelligence

International Journal of Electronics and Communication Engineering

© 2023 by SSRG - IJECE Journal

Volume 10 Issue 3

Year of Publication : 2023

Authors : Tran Ngoc Son, Lai Khac Lai

10.14445/23488549/IJECE-V10I3P101

How to Cite?

Tran Ngoc Son, Lai Khac Lai, "Research on Predictive Control for the Damping System of Autonomous Vehicles in the Public Transport on the Basis of Artificial Intelligence," SSRG International Journal of Electronics and Communication Engineering, vol. 10,  no. 3, pp. 1-9, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I3P101

Abstract:

Autonomous vehicles in public transport can perform a wide range of real-world tasks such as: moving in factories, in public transport, in search and rescue, etc., thus requiring varying degrees of auto-navigation in response to changes in the environment. This paper presents the predictive model (MPC) for the active suspension system, the vehicle's damping system, combined with Deep Q-network (DQN) algorithm reinforcement learning method applied to control self-driving cars in public transport. Intelligent traffic control system to control movement to avoid fixed obstacles or movable obstacles in the event of external interference, taking into account the comfort of the occupants of the vehicle; taking into account the load, the conveying systems; for the purpose of application in the field of intelligent transportation. The research results, built on Matlab Simulink software, show that autonomous vehicles can safely complete intelligent navigation tasks in an unknown environment and become a real system intelligent with the ability to self-study and adapt well to many different environments and various nonlinear factors.

Keywords:

Autonomous vehicle, Active car suspension, Damping system, Model predictive control, Artificial intelligence, DQN algorithm.

References:

[1] N. T. Luy, “Textbook of Machine Learning and Intelligent Control Application,” Publishing House of Industrial University of Ho Chi Minh City. Ho Chi Minh, 2019.
[2] N. T. Tuan, Base Deep learning, The Legrand Orange Book. Version 2, last update, 2020.
[3] Charu C. Aggarwal, “Neural Networks and Deep Learning”, Springer International Publishing AG, part of Springer Nature, 2018.
[4] Boris Houska, Hans Joachim Ferreau, and Mortiz Diehl, “An Auto-generated Realtime Iteration Algorithm for Nonlinear MPC in the Microsecond Range,” Automatica, vol. 47, no. 10, pp. 2279-2285, 2011.
[CrossRef] [Google Scholar] [Publisher link]
[5] Hiep Do Quang et al., “Design a Nonlinear MPC Controller for Autonomous Mobile Robot Navigation System Based on ROS,” International Journal of Mechanical Engineering and Robotics Research, vol. 11, no. 6, pp. 379 - 388, 2022.
[CrossRef] [Google Scholar] [Publisher link]
[6] Lai Thi Thanh Hoa, K. L. Lai, and Tran Ngoc Son, “Model Predictive Control for Vehicle Active Suspension Systems,” Advance in Engineering Research and Applications, pp. 574–580, 2022.
[CrossRef] [Google Scholar] [Publisher link]
[7] P. H. D. Duc, “Predictive control model and application of adaptive control of ship direction,” Journal of Science & Technology of Technical Universities, no. 1(45), pp. 101-106, 2008.
[8] V.T. Minh, and A.A. Rashid, “Modeling and Model Predictive Control for Hybrid Electric Vehicles,” International Journal of Automotive Technology, vol. 13, pp. 477– 485, 2012.
[CrossRef] [Google Scholar] [Publisher link]
[9] Ahmad Reda, Ahmed Bouzid, and Jozsef Vasarhelyi, “Model Predictive Control for Automated Vehicle Steering,” Acta Polytechnica Hungarica, vol. 17, no. 7, pp. 163-182, 2020.
[CrossRef] [Google Scholar] [Publisher link]
[10] Hiep Do Quang et al., “An Approach to Design Navigation System for Omnidirectional Mobile Robot Based on ROS,” International Journal of Mechanical Engineering and Robotics Research, vol. 9, no. 11, pp. 1502-1508, 2020.
[CrossRef] [Google Scholar] [Publisher link]
[11] S. Di Cairano, and I. Kolmanovsky, “Automotive applications of model predictive control, Handbook of Model Predictive Control,” Control Engineering, pp. 493-527, 2019.
[12] Veronica Adetola, and Martin Guay, “Robust Adaptive MPC for Constrained Uncertain Nonlinear Systems,” International Journal of Adaptive Control and Signal Processing, vol. 25, no. 2, pp. 155-167, 2011.
[CrossRef] [Google Scholar] [Publisher link]
[13] Nguyen Tien Ban, “The TUBE-MPC learning predictive control for the uncertain LURE system with the Lipschitz continuum”, Journal of Science, Hai Phong University, no. 46, pp. 45-51, 2021.
[14] Mohammad Ali Nekoui, and Parisa Hadavi, “Optimal Control of an Active Suspension System,” Proceedings of 14th International Power Electronics and Motion Control Conference, EPE-PEMC 2010, pp. 60–63, 2010.
[CrossRef] [Google Scholar] [Publisher link]
[15] Huijun Gao, Weichao Sun, and Peng Shi, “Robust Sampled-data Control for Vehicle Active Suspension System,” IEEE Transactions Control Systems Technology, vol. 18, no. 1, pp. 238–245, 2010.
[CrossRef] [Google Scholar] [Publisher link]
[16] L.T.H. Linh, L. K. Loi, and N. T. M. Huong, “Model-based Predictive Control for Nonlinear Systems with a Forecasting Range of One,” Journal of Science and Technology of Thai Nguyen University, no. 112(12)/2, p. 55-61, 2015.
[17] Xiaogang Ruan et al., “Mobile Robot Navigation based on Deep Reinforcement Learning,” 2019 Chinese Control and Decision Conference, 2019.
[CrossRef] [Google Scholar] [Publisher link]
[18] Hiep Do Quang et al., “Mapping and Navigation with Four-wheeled Omnidirectional Mobile Robot Based on Robot Operating System,” 2019 International Conference on Mechatronics Robotics and Systems Engineering, pp. 54–59, 2019.
[CrossRef] [Google Scholar] [Publisher link]
[19] Nguyen Thi Mai Huong et al., “Research on Optimization Strategies in Predictive Control,” Journal of Science and Technology of Thai Nguyen University, vol. 113, no. 13, pp. 115 - 121, 2013.
[20] Hong Che, and Kong-Hui Guo, “Constrained Control of Active Suspensions: An LMI Approach,” IEEE Transactions Control Systems Technology, vol. 13, no. 3, pp. 412–421, 2005.
[CrossRef] [Google Scholar] [Publisher link]
[21] Yuankai Wu et al., “Deep Reinforcement Learning of Energy Management with Continuous Control Strategy and Traffic Information for a Series-Parallel Plug-in Hybrid Electric Bus,” Applied Energy, vol. 247, pp. 454-466, 2019.
[CrossRef] [Google Scholar] [Publisher link]
[22] Pablo Marin-Plaza et al., “Global and Local Path Planning Study in a ROS Based Research Platform for Autonomous Vehicles,” Journal of Advanced Transportation, pp. 1-10, 2018.
[CrossRef] [Google Scholar] [Publisher link]
[23] Haiping Du, and Nong Zhang, “Control of Active Vehicle Suspensions with Actuator time Delay,” Journal of Sound and Vibration, vol. 301, no. 1-2, pp. 236–252, 2007.
[CrossRef] [Google Scholar] [Publisher link]
[24] Anh Tuan Vo, Thanh Nguyen Truong, and Hee-Jun Kang, “A Novel Prescribed-Performance-Tracking Control System with FiniteTime Convergence Stability for Uncertain Robotic Manipulators,” Sensors, vol. 22, no. 7, 2022.
[CrossRef] [Google Scholar] [Publisher link]
[25] Hamid Khatibi, Alireza Karimi, and Roland Longchamp, “Fixed-order Controller Design for Polytopic Systems using LMIs,” IEEE Transactions on Automatic Control, vol. 53,no. 1, pp. 428–434, 2008.
[CrossRef] [Google Scholar] [Publisher link]
[26] Wen-Kung Tseng, and Hou-Yu Chen, “The Study of Tracking Control for Autonomous Vehicle,” SSRG International Journal of Mechanical Engineering, vol. 7, no. 11, pp. 57-62, 2020.
[CrossRef] [Publisher link]
[27] P. Mahalakshmi, and R. Vallikannu, “New Miniature Dual Mode Rat-race Coupler Design for Autonomous Vehicles,” International Journal of Engineering Trends and Technology, vol. 70, no. 1, pp. 222-233, 2022.
[CrossRef] [Publisher link]