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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.

In this paper, the performance tradeoffs in the design of electronically controlled suspension systems are theoretically studied. Using quarter car model, a new treatment procedure for the control laws is introduced using fully active suspension system with two control strategies. The first strategy is considered for vehicle vibration isolation due to random road excitation only. The second strategy is considered to perform a zero steady-state suspension deflection due to body vehicle attitude variation during maneuvering, braking and aerodynamics as well as vibration isolation due to random road excitation. The two strategies are achieved by using two different optimization techniques combined with PID (Proportional-Integral-Derivative) compensator. The first technique is based on Linear Quadratic Regulation (LQR) technique and the second technique is based on Genetic Algorithm (GA).

The dynamics of vehicles became one of the most important aspects for current developments of electronically controlled steering, suspension and traction/braking systems. However, most of the published research on vehicle maneuverability doesn't take into account the effect of the dynamic tire load and its variation on uneven roads. Clearly, it was stated that using a suitable active suspension system could reduce this dynamic tire load. This dynamic tire load is playing a vital role as it is the major link between the vertical and lateral forces exerted on the road, which affects the lateral dynamics of the vehicle. In this paper, a practical hydro-pneumatic limited bandwidth active suspension system with and without wheelbase preview control is used to study its influence on the vehicle stability in lateral direction. The model is a longitudinal half car with four degrees of freedom.

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.

The early detection of incipient failure in a mechanical system is of great practical importance as it permits scheduled inspections without costly shutdowns and indicates the urgency and locations for repair before a system incurs catastrophic failure. However, in this work a new technique for processing vibration data to quantify the level of damage, cracks only, in a gear system. The technique consists of a nonlinear numerical optimization. The optimization uses a dynamic model of the gear mesh used in vehicle gearbox and forms an estimate of both time-varying and frequency-varying mesh stiffness that best corresponds to the given set of vibration data. The procedure developed in this study can be applied as a part of either an onboard machine health monitoring system or a health diagnostic system used in the regular maintenance.

Squeal of disc brakes is considered as a main source of discomfort for passengers. Typically 1 to 4 kHz noise is considered low frequency squeal and ≻8 kHz noise is considered high frequency squeal. It is a significant problem in passenger vehicles for the comfort of the passengers and a significant financial problem for industry too. Many manufacturers of brake pad materials spend up to fifty percent of their engineering budgets on noise, vibration and harshness (NVH) issues. Squeal noise is strongly correlated to the squeal index and degree of instability of the brake system assembly. Decreasing this squeal noise to some extent during braking is very important matter for the comfort of passengers. So, a mathematical prediction model of 10-degree-of-freedom has been developed to study the effect of different brake components parameters on the degree of instability and squeal index of the brake system.

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.

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.

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.

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.

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.

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.

It is well known that the excessive levels of vibration in heavy vehicles negatively affect driver comfortability, cargo safety and road condition. The current challenge in the field of suspension design for heavy vehicles is to optimize the suspension dynamic parameters to improve such requirements. Almost all of the previous work in this field is based on applying the mathematical optimization considering active or passive suspension systems to obtain the optimal dynamic parameters. In this work a new passive suspension systems for heavy trucks is suggested and compared with the conventional passive suspension systems. The new systems rely on transferring the vertical motion, (vibration), into horizontal motion through a bell-crank mechanism to be taken by a horizontal passive suspension system. The system dynamic parameters like body acceleration, suspension travel and dynamic tire load are calculated assuming random excitation due to road irregularities.

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.

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.