1
Electrical Power Engineering Techniques Department, Bilad Alrafidain University College, Diyala, 32001, Iraq
(dr.naseer@bauc14.edu.iq)
2
AlKafeel University, Iraq
( ali.muhssen@alkafeel.edu.iq)
3
Department of Medical Instrumentation Techniques, Bilad Alrafidain University College, Diyala, 32001, Iraq
(bashar@bauc14.edu.iq)
Abstract :
In autonomous vehicles, the control unit must be based on two main goals, first maintains the stability of the car second follows the desired path. All things considered, the controller's effectiveness is heavily dependent on the details of the steering system actuators. The necessary steering is set by a higher-order controller. The time delay of the steering actuator is one of the main features affecting the performance of the controller. While the artificial intelligence and artificial ethic are new apparatuses in autonomous vehicles but their ICs and electrical component are exposed to fusion. This paper primarily presents a more reliable system work during the fusion of multi-sensor information. We design the requirements of the steering system and the sureness of stability control in autonomous vehicles, also finding the suitable parameters for high-level control algorithms to compensate for time delay and ensure vehicle stability. The vehicle's steering angle response was obtained by testing the actuator of electric power steering (EPS) undergoing different speeds. In fact, using the identification of the system has been beneficial because obtaining the transfer function is easier than the actual methods which need the implementation of a mathematical model of the system. The system response of the Input-output has been defined via MATLAB. Full vehicle model simulation results indicate that the found adjustment parameter improves lane-tracking performance in a basic architecture by reducing oscillation and lateral error relative to other instances. The simplified steering system is the primary improvement brought by this effort.
Keywords :
Autonomous Vehicles; electric power steering; artificial intelligent; Flexible Manufacturing System (FMS); Information Fusion; multi-sensor information; fusion method.
References :
[1] Zhao C., Li L., Pei X., Li Z., Wang F. Y. and Wu, X. "A Comparative Study of State-of-the-Art Driving Strategies for Autonomous Vehicles." Accident Analysis & Prevention 150 (2021): 105937..
[2] Kowshik, Hemant, Derek Caveney, and P. R. Kumar. "Provable System Wide Safety in Intelligent Intersections." IEEE transactions on vehicular technology 60.3 (2011): 804-818.
[3] Narbayeva S., Bakibayev T., Abeshev K., Makarova I., Shubenkova K. and Pashkevich, A. "Blockchain Technology on The Way of Autonomous Vehicles Development" Transportation (2020). Research Procedia, 44, 168-175.
[4] Jung C. H., Kim H. K., Son Y. S., Lee K. W., and Yi K. S. “Parameter Adaptive Steering Control for Autonomous Driving” IEEE 17th International Conference on Intelligent Transportation Systems (ITSC, 2014), pp. 1462-1467.
[5] Zainab Ali Abbood, Ismael Khaleel, & Karan Aggarwal. (2021). Challenges and Future Directions for Intrusion Detection Systems Based on AutoML. Mesopotamian Journal of CyberSecurity, 2021, 16–21. https://doi.org/10.58496/MJCS/2021/004.
[6] Amiri Hossein (2021). "Longitudinal Trajectory Tracking Analysis for Autonomous Electric Vehicles Based on PID Control" (Doctoral dissertation, University of South Florida).
[7] Elmokadem, Taha, Mohamed Zribi, and Kamal Youcef Toumi. "Terminal Sliding Mode Control for The Trajectory Tracking of Underactuated Autonomous Underwater Vehicles." Ocean Engineering 129 (2017): 613-625.
[8] Zhang C., Hu J., Qiu J., Yang W., Sun H., and Che, Q. "A Novel Fuzzy Observer-Based Steering Control Approach for Path Tracking in Autonomous Vehicles". IEEE Transactions on Fuzzy Systems. (2018), 27(2), 278-290.
[9] Park Myungwook, Sangwoo Lee, and Wooyong Han. "Development of Steering Control System for Autonomous Vehicle Using Geometry‐Based Path Tracking Algorithm." Etri Journal 37.3 (2015): 617-625.
[10] Taghavifar Hamid, and Subhash Rakheja. "Path-Tracking of Autonomous Vehicles Using A Novel Adaptive Robust Exponential-Like-Sliding-Mode Fuzzy Type-2 Neural Network Controller." Mechanical Systems and Signal Processing 130 (2019): 41-55.
[11] Phan, D., Bab-Hadiashar, A., Lai, C. Y., Crawford, B., Hoseinnezhad, R., Jazar, R. N., and Khayyam, H. "Intelligent Energy Management System for Conventional Autonomous Vehicles. Energy", (2020). 191, 116476.
[12] Phan D., Bab-Hadiashar A., Hoseinnezhad R., N Jazar R., Jamali A., Pham D. B., and Khayyam, H. "Neuro-Fuzzy System for Energy Management of Conventional Autonomous Vehicles". Energies, (2020). 13(7), 1745.
[13] Hu, Chuan, Yimin Chen, and Junmin Wang. "Fuzzy Observer-Based Transitional Path-Tracking Control for Autonomous Vehicles." IEEE Transactions on Intelligent Transportation Systems (2020).
[14] Yassine Graduate Engineer, Zein. "Fuzzy Logic Velocity Optimizition of Autonomous Vehicles Based on Road Bump Geometry" BAU Journal-Science and Technology 1.2 (2020): 2.
[15] Guo Jinghua, Yugong Luo and Keqiang Li. "An Adaptive Hierarchical Trajectory Following Control Approach of Autonomous Four-Wheel Independent Drive Electric Vehicles." IEEE Transactions on Intelligent Transportation Systems 19.8 (2017): 2482-2492.
[16] Snider J. M. “Automatic Steering Methods for Autonomous Automobile Path Tracking”, Carnegie Mellon University, Pittsburgh, Pennsylvania. 2009.
[17] Lee D. P. and Yi K. S. “Disturbance Adaptive Steering Wheel Torque Control for Enhanced Path Tracking of Autonomous Vehicles” American Control Conference (ACC 2017), pp. 2321-2326.
[18] Pacejka, Hans. Tire and vehicle dynamics. Elsevier, 2nd edition in 2005.
[19] Heredia G. and Ollero A. “Stability of Autonomous Vehicle Path Tracking with Pure Delays in the Control Loop” Advanced Robotics, Vol. 21, (2007) No. 1~2, pp. 23-50.
[20] Chae H. S., Lee M. S. and Yi K. S.“Probabilistic Prediction Based Automated Driving Motion Planning Algorithm for Lane Change”, 17th International Conference on Control, Automation and Systems (ICCAS 2017), pp. 1640-16.
| Style | # |
|---|---|
| MLA | Nasseer K. Bachache, Ali Muhssen Abdul-Sadah, Bashar Ahmed Khalaf. "The Steering Actuator System to Improve Driving of Autonomous Vehicles based on Multi-Sensor Data Fusion." Fusion: Practice and Applications, Vol. 11, No. 1, 2023 ,PP. 77-86 (Doi : https://doi.org/10.54216/FPA.110106) |
| APA | Nasseer K. Bachache, Ali Muhssen Abdul-Sadah, Bashar Ahmed Khalaf. (2023). The Steering Actuator System to Improve Driving of Autonomous Vehicles based on Multi-Sensor Data Fusion. Journal of Fusion: Practice and Applications, 11 ( 1 ), 77-86 (Doi : https://doi.org/10.54216/FPA.110106) |
| Chicago | Nasseer K. Bachache, Ali Muhssen Abdul-Sadah, Bashar Ahmed Khalaf. "The Steering Actuator System to Improve Driving of Autonomous Vehicles based on Multi-Sensor Data Fusion." Journal of Fusion: Practice and Applications, 11 no. 1 (2023): 77-86 (Doi : https://doi.org/10.54216/FPA.110106) |
| Harvard | Nasseer K. Bachache, Ali Muhssen Abdul-Sadah, Bashar Ahmed Khalaf. (2023). The Steering Actuator System to Improve Driving of Autonomous Vehicles based on Multi-Sensor Data Fusion. Journal of Fusion: Practice and Applications, 11 ( 1 ), 77-86 (Doi : https://doi.org/10.54216/FPA.110106) |
| Vancouver | Nasseer K. Bachache, Ali Muhssen Abdul-Sadah, Bashar Ahmed Khalaf. The Steering Actuator System to Improve Driving of Autonomous Vehicles based on Multi-Sensor Data Fusion. Journal of Fusion: Practice and Applications, (2023); 11 ( 1 ): 77-86 (Doi : https://doi.org/10.54216/FPA.110106) |
| IEEE | Nasseer K. Bachache, Ali Muhssen Abdul-Sadah, Bashar Ahmed Khalaf, The Steering Actuator System to Improve Driving of Autonomous Vehicles based on Multi-Sensor Data Fusion, Journal of Fusion: Practice and Applications, Vol. 11 , No. 1 , (2023) : 77-86 (Doi : https://doi.org/10.54216/FPA.110106) |