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The performance of various types of control systems for an electric governor of a diesel engine was examined. The amount of fuel injection of a diesel engine is usually controlled by an electric governor system in these decades, and a PID controller is installed for the electric governor. Even when the optimal parameters for PID controller are well tuned, it is difficult to keep constant rotation speed of the engine, because the applied load to generators may vary according to its running conditions. In this study, a neural network was applied to regulate the parameters in the PID controller for the axial-moving DC motor to control the amount of fuel injection. Experimental studies show that the parameter regulation system using neural network presented here showed good performance under various running conditions. Furthermore, it was shown that various types of training algorithms were applied to neural network control systems and their performance was compared.

Three types of control system for an electric governor of a diesel engine developed for generators were examined experimentally. The quantity of fuel injection of a diesel engine for generators is usually controlled by an electric governor system in these decades, and a PID controller has been installed for the electric governor. Even when the optimal parameters for PID controller are well tuned, it is difficult to keep constant rotating speed of the engine, because the applied load to generators may vary according to its running conditions. In this research, a neural network was applied to regulate the parameters in PI controller for the axial-moving DC motor to control the quantity of fuel injection. Experimental studies showed that the parameter regulation system using neural network presented here had good performance under various running conditions.

This paper deals with a controllable small vibration-proof mount designed for precision devices equipped on cars and other transportation vehicles. The properties of this mount can be controlled by the applied electric field strength, and this is one of application studies of electrorheological(ER) fluid. Liquid crystal is a homogeneous organic compound characterized by molecular orientation responsding to external electric or magnetic field, and because of molecular orientation, its viscosity can be varied by the applied electric field. In the present paper, a mount of small size designed for mass-produced vehicles is constructed and its performance was investigated. It is shown experimentally that the vibration amplitude of a mass supported by the present mount can be decreased by changing the damping properties.

Most of the existing control techniques applied to automobiles are based on a linear model of the relevant dynamic system. However, in spite of the increasing demand for more sophisticated vehicles, very little research is being directed toward understanding and predicting the nonlinear response caused by non-predictable operating conditions such as the variation of suspension characteristics due to wear and tear or the frictional coefficient of the road. In this research, we used a neural network based identification technique to predict the structural response of dynamic systems and validated the technique through comparison with experimental data obtained with a cantilever beam. This identification technique is further extended to forecast the frictional coefficient μ of roads. Numerical results indicate promising future applications.

This paper describes a newly developed prototype engine mount which is electronically controlled for variable spring and damping force. The controllability was accomplished by applying Electro-Rheological (ER) fluid whose apparent viscosity can be varied by applied electric field strength. Main attractive characteristics of ER fluid are wide variation of its apparent viscosity and quick response time, and one of the promising applications is a controllable damper. In the present paper, a prototype engine mount was constructed and its typical performance was investigated, where all the experiments were conducted under constant vibration amplitude to examine the amplitude dependence of the ER characteristics on ER devices. On the basis of the experimental results, numerical simulations of vibration control with ER engine mount and an adaptive neural-net control system were conducted.