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In this paper, a nonlinear semi-active vehicle suspension system using MR fluid dampers is investigated to enhance ride comfort and vehicle stability. Fuzzy logic and fuzzy self-tuning PID control techniques are applied as system controllers to compute desired front and rear damping forces in conjunction with a Signum function method damper controller to assess force track-ability of system controllers. The suggested fuzzy self-tuning PID operates fuzzy system as a PID gains tuner to mitigate the vehicle vibration levels and achieve excellent performance related to ride comfort and vehicle stability. The equations of motion of four-degrees-of-freedom semi-active half-vehicle suspension system incorporating MR dampers are derived and simulated using Matlab/Simulink software.

This work presents a theoretical study on a linear 3-DOF vehicle handling model which, incorporates driver's operation and vehicle suspension derivatives. The model is constructed to investigate the performance of vehicles with conventional front steering system and vehicles with actively controlled front wheel steering system as well. The control strategy of the Active Front Steering (AFS) control is based on the optimal control theory using LQR technique. The vehicle model excitation is a simulation of the aerodynamic forces and moments generated on a passenger car when overtaking a truck. Results are showing a comparison between the performance of the vehicle with conventional steering system and the vehicle with Active Front Steering (AFS) system. A significant improvement with the AFS optimal system is achieved in the vehicle response especially for lateral deviation error.

This research aims to model and assess autonomous vehicle controller while including a four-wheel steering and longitudinal speed control. Such a modeling process simulates human driver behavior with consideration of real vehicle dynamics’ characteristics during standard maneuvers. However, a four-wheel steering control improves vehicle stability and maneuverability as well. A three-degree of freedom bicycle model, lateral deviation, yaw angle, and longitudinal speed is constructed to describe vehicle dynamics’ behavior. Moreover, a comprehensive traction model is implemented which includes an engine, automatic transmission, and non-linear magic formula tire model for simulation of vehicle longitudinal dynamics. A combination of proportional integral derivative (PID) longitudinal controller and fuzzy lateral controller are implemented simultaneously to track the desired vehicle path while minimizing lateral deviation and yaw angle errors.