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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.

Exhaust emissions behavior from a vehicle under road driving condition is affected by a driving force of a vehicle. It is impossible to measure the driving force by an existing torque meter under this on-road driving condition. This study reports the development and verification of the wheel torque meter which is possible to measure driving force under the on-road driving condition and has enough strength and accuracy. The wheel torque meter developed in this study consists of a torque detector with a strain gauge type torque transducer, a telemeter receiver and a data logger. The strain gauge type torque transducer is equipped between hub and wheel at the drive axle tire, because of the narrow width of this transducer, and it is possible to be placed against vehicle without significant conversion of the vehicle. Because of these characters, this transducer is possible to be used for an on-road driving vehicle.