IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 10 NO. 6 2024
DOI: 10.56201/ijcsmt.v10.no6.2024.pg1.14

---

Analysis Of the Geometrical Framework on the Vehicle's Moving Stability Using Expert System Controller

Engr (Mrs) Chidinma Ndukwe & Dr Ehibe Prince

---

Abstract

This research is pivotal because an expert system is used to affect the vehicle's stability, which implies enhancing the stability and safety of vehicles. A dynamic model of the vehicle consisting of a guided front wheel and a free rear wheel is established based on the model of a four-wheeled vehicle when the vehicle moves on the curve road. Expert systems are computer programs that use artificial intelligence methods to solve problems within a specialized domain that ordinarily requires human expertise. The geometrical framework and dynamics parameters, including the factors of moving stability, vehicle moving speed, lateral stiffness parameters of the front and rear wheels, vehicle mass, and vehicle length, on the vehicle's moving stability and safety are then simulated and analyzed, respectively. Expert systems accumulate experience and facts in a knowledge base and integrate them with an inference rules engine a set of rules for applying the knowledge base to situations provided to the program. The research and simulation results show that the stability of the vehicle can be improved, and the operating parameters of the vehicle greatly affect its moving stability. The lateral stiffness parameters of the front and rear wheels should be increased, while the vehicle's mass needs to be reduced in the operating condition of the vehicle to enhance the vehicle's moving stability.

keywords:

Expert system, stability, vehicle, intelligent system, artificial intelligent

---

References:

Abdelmoniem, A.; Osama, A.; Abdelaziz, M.; Maged, S.A (2020). A path-tracking algorithm

using predictive Stanley lateral controller. Int. J. Adv. Robot. pp, 19-33,
Burgos, E.; Bhandari, S. (2016) Potential flow field navigation with virtual force field for UAS

collision avoidance. In Proceedings of the 2016 International Conference on Unmanned

Aircraft Systems (ICUAS), Arlington, VA, USA, 7–10 pp. 505–513
Cabezas-Olivenza, M.; Zulueta, E.; Sanchez-Chica, A.; Teso-fz-Betoño, (2021) A.; Fernandez-

Gamiz, U. Dynamical Analysis of a Navigation Algorithm. pp, 9-31
Chang, L.; Shan, L.; Jiang, C.; Dai, Y. Reinforcement based mobile robot path planning with

improved dynamic window approach in unknown environment. Auton. Robots 2021,
Chen, Y., Chen, L., Wang, J., & Sun, Y. (2015). Vehicle lateral stability control is based on the

integrated control of active front steering and direct yaw moment control. Vehicle System

Dynamics, 53(3), 316-335.

Danciu, A., Faur, M., Tar, J. K., & Iclodean, C. (2017). The influence of wheel alignment on

vehicle dynamics. Procedia Engineering, 181, 1069–1074.
Gao, W., Wang, L., Cheng, J., & Rakheja, S. (2013). Analysis and control of vehicle lateral

stability with active front steering and differential braking. Vehicle System Dynamics,

51(12), 1785–1802.
Gong, F.T., and J.Y. Wang, (2012). Research on weighted fuzzy fault diagnosis based on

adaptive
neural networks. Int. J. Digital Content Technol. Appl., 6: 118–124.
Hedrick, K. J., & Zhang, W. B. (2012). Nonlinear control for vehicle lane-keeping with

disturbance rejection. Vehicle System Dynamics, 50(1), 141–159.
Henkel, C.; Xie, L.; Stol, K.; Xu, W. (2018) Power-minimization and energy-reduction

autonomous navigation of an omnidirectional Mecanum robot via the dynamic window

approach local trajectory planning. Int. J. Adv. Robot. 15, 1–12.
Kim, S., Yi, K., & Sunwoo, M. (2016). A study on the influence of suspension geometry on

vehicle dynamics using design experiments. International Journal of Automotive

Technology, 17(2), 285-294.
Maarif, A.; Rahmaniar, W.; Vera, M.A.M.; Nuryono, A.A.; Majdoubi, R.; Cakan, A (2012).

Artificial Potential Field Algorithm for Obstacle Avoidance in UAV Quadrotor for

Dynamic Environment. In Proceedings of the 2021 IEEE International Conference on

Communication, Networks and Satellite (COMNETSAT),
Rakheja, S., Langari, R., & Vaishya, M. (2018). Developments in Road Vehicle System

Dynamics: Application of Vehicle Dynamics Concepts for the Development of Intelligent

Vehicles. CRC Press.
Tesfazgi, S. Lederer, A.; Hirche, S (2021). Inverse Reinforcement Learning: A Control

Lyapunov Approach. In Proceedings of the 2021 60th IEEE Conference on Decision and

Control (CDC), Austin, TX, USA, 14–17 ; pp. 3627–3632.
Zhang, F You, S.; Diao, M.; Gao, L.; (2020,).; Wang, H. Target tracking strategy using deep

deterministic policy gradient. Appl. Soft Comput. pp. 50–512
Zhang, L., Y.W. Shi, and L.Q. Ren, (2012). Humanoid extraction of abnormal engine sounds by

using ICA-R and VANC. Proceedings of the International Conference on Systems and

Informatics, May 19–20, 2012, Yantai, China, pp. 1687–1692.
Zhang, W., (2011). Based on expert systems in automotive engine fault diagnosis, Master

Thesis, Taiyuan University of Technology, Taiyuan, China.

Zhu, Q., A.R. Huang, and J. Bao, (2010). Design and implementation of an automobile fault

diagnosis expert system. J. Hubei Automotive Indus. Inst., 24: 70–74.

DOWNLOAD PDF

---

Back