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This study presents a practical theoretical method to judge the aerodynamic response of buses in the early design stage based on both aerodynamic and design parameters. A constant longitudinal velocity 2-DOF vehicle lateral dynamics model is used to investigate the lateral response of a bus under nine different wind gusts excitations. An appropriate 3-D CFD simulation model of the bus shape results is integrated with carefully chosen design parameters data of a real bus chassis and body to obtain vehicle lateral dynamic response to the prescribed excitations. Vehicle model validity is carried out then, the 2-DOF vehicle lateral dynamics model has been executed in MATLAB Simulink environment with the selected data. Simulation represents the vehicle in a straight ahead path then entered a gusting wind section of the track with a fixed steering wheel. Vehicle response includes lateral deviation (LD), lateral acceleration (LA), yaw angle (YA) and yaw rate (YR).

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.

The bearing capacity of soils stands as one of the most important parameters that determine the vehicles’ off-road mobility. Soil bearing capacity can be determined either experimentally or by calculation using analytical and or empirical formulas. One of the most famous formulas is the Bekker's. Recently, Artificial Neural Networks (ANNs) technique became a powerful tool that can be used for predicting systems’ behavior and performance. The main objective of this paper is to predict the bearing capacity of the soil (plate-sinkage relationships) by using Artificial Neural Networks and to compare the actual results of soil bearing capacity (collected data from Ph.D. Thesis) with results obtained from neural network model and Bekker's formula. The comparison showed clear superiority and accuracy of neural network technique. Another objective is to check the generalization ability of the neural network model in predicting the plate-sinkage relationships by using the hypothetical plate.

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.

Dynamic modeling of the gear vibration is a useful tool to study the vibration response of a geared system under various gear parameters and operating conditions. An improved understanding of vibration signal is required for early detection of incipient gear failure to achieve high reliability. However, the aim of this work is to make use of a 6-degree-of-freedom gear dynamic model including localized tooth defect for early detection of gear failure. The model consists of a gear pair, two shafts, two inertias representing load and prime mover and bearings. The model incorporates the effects of time-varying mesh stiffness and damping, backlash, excitation due to gear errors and modifications. The results indicate that the simulated signal shows that as the defect size increases the amplitude of the acceleration signal increases. The crest factor and kurtosis values of the simulated signal increase as the fault increases.

The DC power supply is ingredient part in the automotive industries as it has been used as a DC power supplies for a wide range of loads. Meanwhile, it is mandatory for battery charging. These types however, causes many problems such as poor power factor, high input current harmonics distortion and uncontrolled DC voltage. In this paper, an improved input power factor correction that uses a combined control system consists of two nested loops with a feedback of the DC voltage and input current as long as a feed forward from the output power. The system has been analyzed, modeled, simulated and experimentally verified. The novel feature of the proposed control scheme resides in fact that it is not only achieve nearly unity power factor with minimum input current total harmonics distortion only but it also introduce superior performance in DC voltage transient conditions.

In this paper, a hybrid roll control system, including passive and active roll control units, is designed to improve the roll dynamics of tanker vehicles and to reduce the lateral shifts of the liquid cargo due to lateral accelerations. The passive control system consists of radial partitions installed inside the vehicle container. These partitions rotate in phase with the liquid cargo as one unit about the longitudinal axis of the container in response to the induced momentum forces due to the lateral acceleration excitation. Torsion dampers are fixed between the partitions and the container's front and rear walls to reduce the oscillating motion of the liquid cargo. While the passive partition dampers control the dynamics of the liquid cargo inside the container, the dampers of the vehicle suspension are switchable, generating anti-roll damping moments based on the lateral acceleration level and the container filling ratio.

The previous research work on Active Independent Front Steering (AIFS) system concluded an enhanced vehicle response and tire adhesion utilization. Some research emphasizes the importance of Tire Work load (TWL) in the generation of maximum possible tire forces that ensures vehicle controllability and stability. In this study, a mathematical model is constructed to investigate the effect of TWL as a parameter on AIFS performance. Toward such a target, a new Fuzzy control strategy is developed based on TWL and vehicle yaw rate as control inputs for the AIFS controller. Unfortunately, the TWL is not a measurable parameter or even easy to be estimated. Consequently, another control strategy was implemented based on slip angle and vehicle yaw rate as inputs for the AIFS controller.

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.

The anti-lock brake system (ABS) is a vital system in modern vehicles that prevents automotive wheels from locking during an emergency brake. This paper aims to introduce an efficient, optimized proportional integral derivative (PID) controller tuned using a genetic algorithm (GA) to enhance the performance of ABS. The PID control method is a very famous control algorithm employed in numerous engineering applications. The GA is used to solve the nonlinear optimization problem and search for the optimum PID controller gains by identifying the solution to the problem. A mathematical model of ABS is derived and simulated using Matlab and Simulink software. The proposed optimized PID-controlled ABS is compared to the conventional ABS controlled using a Bang-Bang controller. System performance criteria are evaluated and assessed under different road adhesion coefficient values to judge the success of the proposed PID controller tuned using GA.

In this paper, a fuzzy-PID controller is applied in a half vehicle active suspension system to enhance vibration levels of vehicle chassis and passenger seat. The fuzzy-PID controller consists of fuzzy and PID connecting in a series manner, the fuzzy output is considered as the PID input. Genetic Algorithm (GA) is selected to tune controller parameters to obtain optimal values that minimize the objective function. The equations of motion of five-degrees-of-freedom active half-vehicle suspension system are derived and simulated using Matlab/Simulink software. Double bumps and random road excitations are used to study the performance of suspension systems including bounce and pitch motion. The performance of the active suspension system using optimized fuzzy-PID controller is compared with conventional passive to show the efficiency of the proposed active suspension system.

Welding is an excellent attachment or repair method. The advanced industries such as oil, automotive industries, and other important industries need to rely on reliable welding operations; collapse because of this welding may lead to an excessive cost in money and risk in human life. In the present research, an automatic system has been described to detect, recognize and classify welding defects in radiographic images. Such system uses a texture feature and neural network techniques. Image processing techniques were implemented to help in the image array of weld images and the detection of weld defects. Therefore, a proposed program was build in-house to automatically classify and recognize eleven types of welding defects met in practice.

Integral Control strategy for vehicle chassis systems had been of great interest for vehicle designers in the last decade. This paper represents the interaction of longitudinal control and lateral control. In other words the traction control system and handling control system. Definitely, tire properties are playing a vital role in such interaction as it is responsible for the generated forces in both directions. A seven degrees of freedom half vehicle model is derived and used to investigate this interaction. The vehicle body is represented as a rigid body with three degrees of freedom, lateral and longitudinal, and yaw motions. The other four degrees are the two rotation motion of the front wheel and the rear wheel. This two motions for each wheel are spin motion and the steering motion. The traction controller is designed to modulate engine torque through adjusting the throttle angle of the engine upon utilized adhesion condition at the driving road wheels.

A non-linear mathematical model of a semi-active (2DOF) vehicle suspension using a magnetorheological (MR) damper with information concerning the road profile ahead of the vehicle is proposed in this paper. The semi-active vibration control system using an MR damper consists of two nested controllers: a system controller and a damper controller. The fuzzy logic technique is used to design the system controller based on both the dynamic responses of the suspension and the Padé approximation algorithm method of a preview control to evaluate the desired damping force. In addition, look-ahead preview of the excitations resulting from road irregularities is used to quickly mitigate the effect of the control system time delay on the damper response.

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.

Proportional-integral-derivative (PID) controller is effective, popular and cost effective for a lot of scientific and engineering applications. In this paper, PID and fuzzy-self-tuning PID (FSTPID) controllers are applied to improve the performance of an active seat suspension system to enhance the pregnant woman comfort. The equations of motion of thirteen-degrees-of-freedom (13-DOF) active seat suspension system incorporating pregnant woman body model are derived and simulated. PID gains are tuned and estimated using genetic algorithm (GA) to formulate GA PID controller. In FSTPID, fuzzy logic technique is used to tune PID controller gains by selecting appropriate fuzzy rules using Matlab/Simulink software. Both controlled active seat suspension systems are compared with a passive seat suspension. Suspension performance is evaluated under bump and random road excitations in order to verify the success of the proposed controllers.

The present paper presents design considerations for a new tandem suspension system equipped with hydro-pneumatic components. The theory of the new suspension and its configuration were presented in a previously published SAE paper, [1]. In this design, most of the vertical motions were transformed into horizontal motions through two bell cranks. A hydraulic actuator is installed horizontally between the bell cranks and connected to an accumulator (gas spring) via a flow constriction (damper). Incorporating of hydro-pneumatic components in the new suspension system exhibits simple and applicable design. Moreover, further developments including active or semi-active vibration control systems, can be applied directly using the existing hydro-pneumatic components. Mathematical models are constructed to simulate the vehicle ride dynamics. Equations of motion are generated considering a conventional passive suspension (four springs tandem suspension) and the new designed suspension system.

In this paper, semi-active MR main suspension system based on system controller design to minimize pitch motion linked with MR-controlled seat suspension by considering driver’s biodynamics is investigated. According to a fixed footprint tire model, the transmitted tire force is determined. The linear-quadratic Gaussian (LQG) system controller is able to enhance ride comfort by adjusting damping forces based on an evaluation of body vibration from the dynamic responses. The controlled damping forces are tracked by the signum function controllers to evaluate the supply voltages for the front and rear MR dampers. Based on the sprung mass acceleration level and its derivative as the inputs, the optimal type-2 (T-2) fuzzy seat system controller is designed to regulate the controlled seat MR damper force.

Proportional integral derivative (PID) control technique is a famous and cost-effective control strategy, in real implementation, applied in various engineering applications. Also, the ant colony optimization (ACO) algorithm is extensively applied in various industrial problems. This paper addresses the usage of the ACO algorithm to tune the PID controller gains for a semi-active heavy vehicle suspension system integrated with cabin and seat. The magnetorheological (MR) damper is used in main suspension as a semi-active device to enhance the ride comfort and vehicle stability. The proposed semi-active suspension consists of a system controller that calculate the desired damping force using a PID controller tuned using ACO, and a continuous state damper controller that predict the input voltage that is required to track the desired damping force.