Revolution Control for Diesel Engines by Neural Networks

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.