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In this work the effects of vehicle vertical vibrations on the tires/road cornering forces, and then consequently on vehicle lateral dynamics are studied. This is achieved through a ride model and a handling model linked together by a non-linear tire model. The ride model is a half vehicle with four degrees of freedom (bounce and pitch motions for vehicle body and two bounce motions for the two axles). The front and rear suspension are a hydro-pneumatic slow-active systems with 6 Hz cut-off frequency designed based on linear optimal control theory. Vehicle lateral dynamics is modeled as two degrees (yaw and lateral motions) incorporating a driver model. An optimal rear wheel steering control in addition to the front steering is considered in the vehicle model to represent a Four Wheel Steering (4WS) system. The tire non-linearity is represented by the Magic Formula tire model.

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.

Adaptive neuro-fuzzy inference system (ANFIS) technique has been developed and applied by numerous researchers as a very useful predictor for nonlinear systems. In this paper, non-parametric models have been investigated to predict the direct and inverse nonlinear dynamic behavior of magnetorheological (MR) fluid dampers using ANFIS technique to demonstrate more accurate and efficient models. The direct ANFIS model can be used to predict the damping force of the MR fluid damper and the inverse dynamic ANFIS model can be used to offer a suitable command voltage applied to the damper coil. The architectures and the learning details of the direct and inverse ANFIS models for MR fluid dampers are introduced and simulation results are discussed. The suggested ANFIS models are used to predict the damping force of the MR fluid damper accurately and precisely. Moreover, validation results for the ANFIS models are proposed and used to evaluate their performance.

1 Rear wheel drive vehicles have a long driveline using a propeller shaft with two universal joints. Consequently, in this design usage of universal joints within vehicle driveline is inevitable. However, the angularity of the driveshaft resulting from vertical oscillations of the rear axle causes many torsional and bending fluctuations of the driveline. Unfortunately, most of the previously published research work in this area assume the propeller inclination angle is constant under all operating conditions. As a matter of fact, this assumption is not accurate due to the vehicle body attitudes either in pitch or bounce motions. Where the vehicle vibration due to the suspension flexibility, either passive or active type, exists.