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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.

The study investigates the ride comfort of a rail vehicle with semi-active suspension control and its effect on train vertical dynamics. The Harmony Search algorithm optimizes the gains of a proportional integral derivative (PID) controller using the self-adaptive global best harmony search method (SGHS) due to its effectiveness in reducing the tuning time and offering the least objective function value. Magnetorheological (MR) dampers are highly valuable semi-active devices for vibration control applications rather than active actuators in terms of reliability and implementation cost. A quarter-rail vehicle model consisting of six degrees of freedom (6-DOF) is simulated using MATLAB/Simulink software to evaluate the proposed controller's effectiveness. The simulated results show that the optimized PID significantly improves ride comfort compared to passive.

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.

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.

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.

A full vehicle of a preview control semi-active suspension system based on an interval type-2 fuzzy controller design using a magnetorheological (MR) damper to improve ride comfort is investigated in this paper. It is integrated with the force distribution system to obtain the optimal rate of road adhesion during braking and handling. The nonlinear suspension model is derived by considering vertical, pitch, and roll motions. The preview interval type-2 fuzzy technique is designed as a system controller, and it is attached with a Signum function method as a damper controller to turn on the voltage for the MR damper. This voltage is adjusted for each wheel based on the external excitation generated by road roughness in order to enhance ride comfort. To describe the effectiveness and adaptable responses of the preview controlled semi-active system, the performance is compared with both the passive and MR passive suspension systems during time and frequency domains.

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.

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.

This study deals with the mitigation of the vibration of the structure using a compound mass liquid column damper (MLCD). To study the damping efficiency of the MLCD, the mathematical model of the single degree of freedom structure integrated with MLCD including the damping due to the moving mass in the horizontal portion of the damper is derived. The equivalent-damping factor of the MLCD is determined by simulating the interaction between the moving mass and the liquid using the fluid-structure interaction (FSI) technique. A 3D FSI model is solved numerically using the ANSYS Workbench-CFX package. The global search optimization algorithm is applied to find the optimum tuning frequency and the optimum mass diameter ratio over a wide range of mass ratio and excitation amplitude. The optimization issue is solved with considering the limits of the maximum displacement of the liquid and the mass.

The minimum support stiffness that achieves the maximum modal frequencies or critical speed is very important in the design of mechanical systems. The optimal values of the intermediate support stiffness and geometrical parameters of uniform and stepped Timoshenko beams composed of single or two materials are studied in order to maximize the modal frequency and minimize the intermediate support stiffness. Dynamic stiffness matrix (DSM) method and multi-objective particle swarm optimization (MOPSO) algorithm are used together to evaluate new optimal parameters. For single material, the results show that for uniform thick beams, the optimal maximum fundamental frequency and minimum intermediate support stiffness are lower than those of Bernoulli-Euler beams. In addition, the optimal design for stepped beams made of two metallic materials is investigated. For three different metallic combinations, gain factors of 1.561 to 2.745 are obtained for a beam without intermediate support.

This paper introduces an optimum design for a feedback controller of a fully active vehicle suspension system using the combined multi-objective particle swarm optimization (CMOPSO) in order to minimize the actuator power consumption while enhancing the ride comfort. The proposed CMOPSO algorithm aims to minimize both the vertical body acceleration and the actuator power consumption by searching about the optimum feedback controller gains. A mathematical model and the equations of motion of the quarter-car active suspension system are considered and simulated using Matlab/Simulink software. The proposed active suspension is compared with both active suspension system controlled using the linear quadratic regulator (LQR) and the passive suspension systems. Suspension performance is evaluated in time and frequency domains to verify the success of the proposed control technique.

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.

Proportional integral derivative (PID) control method is an effective, easy in implementation and famous control technique applied in several engineering systems. Also, Genetic Algorithm (GA) is a suitable approach for optimum searching problems in science, industrial and engineering applications. This paper presents the usage of GA for determining the optimal PID controller gains and their implementation in the active quarter-vehicle suspension system to achieve good ride comfort and vehicle stability levels. The GA is applied to solve a combined multi-objective (CMO) problem to tune PID controller gains of vehicle active suspension system for the first time. The active vehicle suspension system is modeled mathematically as a two degree-of-freedom mechanical system and simulated using Matlab/Simulink software.

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).

Nonparametric models do not require any assumptions on the underlying input/output relationship of the system being modeled so that they are highly useful for studying and modeling the nonlinear behaviour of Magnetorheological (MR) fluid dampers. However, the application of these models in semi-active suspension is very rare and most theoretical works available on this topic address the application of parametric models (e.g. Modified Bouc-Wen model). In this paper, a nonparametric MR damper model based on the Restoring Force Surface technique is applied in vehicle semi-active suspension system. It consists of a three dimensional interpolation using Chebyshev orthogonal polynomial functions to simulate the MR damper force as a function of the displacement, velocity and input voltage. Also, a damper controller based on a Signum function method is proposed, for the first time, for use in conjunction with the system controller of a semi-active vehicle suspension.

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.

The number of speed humps (sleeping policemen) has seen a global increase in the last decade. This paper addresses the geometric requirements of these humps using Genetic Algorithms optimization techniques to control the speed, stability, and ride feel of the traversing vehicles. The interaction between road hump profile and the modeled vehicles (passenger and a two-axle truck) are studied with a dynamic model. The shape of the proposed profile is described by numbers of amplitudes of harmonic functions. The extreme acceleration of the drivers’ seats of the vehicles traversing the hump is set as multiobjective function for the optimization process, taking into consideration the road-holding ability represented by the tire lift-off speed. The results show that hump geometry can be improved while fulfilling the requirements of speed control and vehicle dynamic responses.

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.

A hybrid search optimization is presented in order to optimize hybrid laminated fibrous composite E-springs for vehicle suspension systems. This optimization is conducted with both of the geometrical configuration and laminate structure of the E-spring. A genetic algorithm along with a hill-climbing random-walk approach are used through a developed NURBS-based technique in order to conduct this optimization. A mathematical-modeling-based mid-ware technology is introduced in order to fully automate the optimization process through linking the run engines of mathematical modeling and finite element analysis from within the mathematical modeling engine. A hybrid approach of the inter-laminar shear stress and Tsai-Wu criteria is first implemented in order to identify failure indices of the resulting optimum shape and laminate structure.