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To develop a front steering control system, a nonlinear steering control law is necessary, since the dynamical characteristics of cars are nonlinear at the situation of large side slip angle and high yaw rate. The robustness to the change of friction coefficient μ is required on the controller. In this study, an intelligent control of front wheel steering is presented. The controller consists of 2 neural network controllers for specific values of μ and an integrator under the Cubic Neural Network (CNN) architecture. The neural networks are designed with error back propagation learning. By using the CNN architecture, the controller can adapt to various values of μ. The effectiveness and the feasibility of the present active steering method are demonstrated by numerical simulations using simple 8DOF model and DADS full vehicle model.

In this study, we investigated the relationship of indicated mean effective pressure (IMEP) for a spark ignition engine under lean combustion with the cyclic variation of mass fraction burned by measuring the energy release from the spark plug, intensity of the light emmision from the flame and the cylinder pressure at the same time. In order to minimized an error in the initial and late combustion stages of the mass fraction burned to be obtained by cylinder pressure, spark plug energy and intensity of light emission were measured. As a result, it was found that there are three main couses of cyclic variation of IMEP. These consist of the burning speed during the initial stage of combustion, variation in the total mass fraction burned, and variation of the late burning during the late expantion stroke. Thus, we determined that there is a favorable interrelatonship between the IMEPs and the corrected mass fraction burned.

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.

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.

The droplet velocity, size and distributions of iso-octane fuel from single hole and twin jet air-assist injectors have been measured by phase Doppler velocimetry in the pent-roof for two cylinder head designs of firing four-valve engines running at 1500 rpm, together with the airflow during induction and compression. The use of the twin jet air-assist injector together with the introduction of a transfer-passage between the two intake ports of a Honda VTEC-E valve train arrangement resulted in reduction in ISNOx and COV-1mep of the order of half of those with the single hole injector design without a transfer passage. Droplets, for both heads and injectors, having passed the inlet valves, impinged directly onto the sleeve opposite to their entry without striking the exhaust valves and had velocities up to 30 m/s and Sauter mean diameters which varied from 20 to 50pm.