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In this study, the air suspension is newly applied to the engine mounting layout for getting the significant vibration isolation effect. In this case, the genetic algorithm so called GA is also applied for the optimization of many parameters, calculations of stiffness matrix and inverse stiffness matrix to prevent the coupled vibration of lateral and rolling modes and to obtain the displacement of each mounting point. As a result, inexperienced engineers can easily obtain the optimum engine mounting layout in a minute. By the confirmation test of FEM, the engine lateral vibration level at 25Hz dropped below 1/10 and its effect was significant.

A finite element model for an entire frame-structured sports utility vehicle was made to evaluate the characteristics of the idling vibrations for the vehicle. The engine exciting forces were determined by Souma's method to simulate the idling vibrations. The modeling of the power plant and the entire vehicle was verified by the reasonable agreement of the experiment and calculation results. Attention was focused on the frequency of the first-order vertical bending mode for the frame. It has become clear that the idling vibration level of the vehicle is lowered by decreasing the frequency of the first-order frame bending mode.

State-space solutions of H∞ controller have been well developed. Hence to a real structure control design, the first step is to get a state space model of the structure. There are analytical and experimental dynamic modeling methods. As we know, it is hard to obtain an accurate model for a flexible and complex structure by FEM(Finite Element Method). Then the experimental modeling methods are used. In this paper, we use frequency domain modal analysis technique based on system FRF(Frequency Response Function) data and ERA(Eigensystem Realization Algorithm) time domain method based on system impulse response data to establish state-space model in order to design H∞ control law for the purpose of vibration suppression. The robust control implementation is exerted on a testbed (truck cab model device) with three degrees of freedom. The validity of experimental state-space modeling is testified and the obvious vibration control performances are achieved.

This paper presents some considerations and an application to automotive's subframe concerning system identification method by characteristic matrices using modal parameters to obtain rigid-body properties of mechanical structure. The pendulum method was adopted as the conventional way to identify the rigid-body properties in automotive's engine components such as flywheel. In the case of the big, complex shaped and heavy structural component such as frame and vehicle body, this method is not adequate for the difficulty of building the huge fixture and making sure of the accuracy of identification. So, in this paper, the method identifying characteristic matrices using the modal parameters is applied to automotive's subframe.

A new method is introduced here to identify modal parameters by fitting curves with multiple degrees of freedom to experimental data in frequency domain. Unknown parameters in our method are natural frequencies and modal damping ratios, and, from these parameters natural modes and residual terms are determined. In comparing our method with the conventional Van Loon's method, fewer unknown parameters are dealt with, and thus computing time is shorter with better convergence of solution. Simulation results obtained show that our method is useful especially in refering to many data simultaneously.

Two kinds of active control systems, using neural networks (NN), are presented for realizing optimal driving motion of four wheel steer (4WS) cars. The first system is based on the assumption that the car is simplified as a linear two wheel bycycle model, and that the friction force between tire and road surface is represented by Fiala's nonlinear model. The nonlinear relation between the slip angle of tire and the cornering force is expressed with NN. A model-following type control strategy is adopted in the first system, with both the feedforward and feedback gains for the control of the rear wheel steering angle adaptively determined with NN according to change of front wheel steering angle. The second system is based on the assumption that both the dynamical characteristics of the car and the tire friction force are nonlinear. The nonlinear dynamical characteristics of the car and the friction force are identified with NN, using the measured data of an actual car.

Two methods have been developed for real time identification and classification of the roughness pattern of road surfaces using the neural network. These methods are directly available both for semi-active and active vibration controls of cars. Accelerations of the rear wheel axis under the suspension are used as the input data for real time identification. The neural network which has acquired the informations of the seven typical roughness patterns is used for real time classification of actual road surfaces during driving. Validity and usefulness of these methods are verified by simulation.

There are two analytical methods for optimizing automotive structural dynamic characteristics to improve vehicle ride quality and minimize structural mass for improved fuel economy. The first method, the traditional approach, is to move the undesired structural resonant frequencies out of the range of the forcing functions by modifying the mass and stiffness parameters appropriately. However, in some cases the resonant frequencies are insensitive to parameters; these cases normally are difficult to improve. Fortunately, there is a second method, based on the natural phenomena that an anti-resonance exists for each resonant frequency. Furthermore, the sensitivity of these anti-resonance nodes to the structural parameters of mass, stiffness and damping are uniquely different. It is this difference in sensitivity that permits cases to be solved, which resist solution by the traditional first method.

‘Three kinds of new simultaneous optimum design methods of plural dynamic absorbers are proposed. These methods allow the optimum tuning in many natural modes of multiple degrees of freedom structures or a continuous bodies simultaneously to effectively suppress vibration. Changes of natural modes and natural frequencies of the main structure due to added mass effect of dynamic absorbers can be taken into account in the design. Validity and usefulness of the proposed methods are verified by both a computer simulation and by experiments.

This paper describes a new method for engine design using dynamic optimization and substructure synthesis method. A very important theme in engine design is how to shift the peak of the natural frequency of the vibration mode that causes some noise and vibration problems. This must be resolved by effective modification of structural design. In order to carry out effectively vibration analysis of a large scaled structure like engine assembly and conduct dynamic optimization with many iterative calculations, we have used substructure synthesis method that devides a whole structure into a number of substructures and solves each substructure. Vibration analysis of engine assembly (cylinder block, crank shaft, bearing caps and flywheel systems) was carried out by using this substructure synthesis method.

Hydraulic engine mounts(HEM) are replacing conventional rubber mounts to provide better ride quality and to reduce noise. However, detailed analysis of the HEM is needed to predict ideal performance conditions. In this study, the optimum design of a HEM is modelled using design optimization theory for a dynamic absorber. After determining ideal behavior by simulation, an experimental mounts is designed and tested to verify the model.

In this paper, a new method which directly identifies characteristic matrices (the mass, damping and stiffness matrices) of the mechanical structure using measured forces input and responses data is proposed. This algorithm is based upon the Maximum Likelihood Estimation, so that the accuracy of identified matrices is stable to experimental errors (random errors). After a theoretical formulation is performed, two examples are provided to illustrate and validate this algorithm. One is analytical example which identifies analytically generated data with random noises, and the other experimentarly identified engine/mount system of automobiles.

This paper concerns the Direct System Identification Method (hereafter referred to as DSIM) which allows accurate and quick determination of two groups of properties which exercise dominant effects on low frequency vibration of a vehicle body. The first group is the rigid body properties of an engine. The second group is the properties of each engine mount. Under the assumption that the engine/mount system is a rigid body, this paper makes theoretical discussion for using the DSIM to induce the parameters of an engine/mount system, and makes improvements for better correlation with experiments. Also mentioned is a comparison of this study with the experimental results and verification of consistency on those parameters obtained from DSIM to predict the accurate vehicle characteristics, along with the role this method will play in upgrading the technology of prediction analysis.