The Simulation Research On The Speed Control Of Marine Diesel Engine

Nowadays,diesel engine is the main power plant in the industry,transport,etc. Therefore,the research on the integrated control of diesel engine for running at the best tradeoff point of economy,power and emissions would be of great significance to energy-saving and environmental protection.This paper mainly research on the speed control of diesel engine,try to make the output track the input so that the diesel engine can run in an optimal state.At present,PID controller is mainly used in the speed control of engine.It is simple,and contented with the most control requirements of the control system,while it is no longer able to achieve the desired control performance for nonlinear and time-varying system.The traditional PID controller can not change the PID parameters in the control process and the control performance is not satisfied.The idea of fuzzy control and neural network control were represented for the shortcomings of PID controller.The fuzzy PID controller and neural network PID controller were designed so that the three parameters of PID controller can be changed according to the actual operation state of the control system in the simulation procedure,thereby improving the performance of the control system.The basic principles and design steps of fuzzy PID control algorithm and neural network PID control algorithm which based on the details of the fuzzy control theory and principles of artificial neural network is given in this paper.A large number of simulation tests for the speed feedback control system of the diesel engine were carried out in this paper,using the model of average large-scale low-speed marine diesel engine with Matlab/Simulink software.The simulation results show that the fuzzy PID control performance have been greatly improved on the aspects of overshoot and regulation time compared with conventional PID control.They also show that the neural network PID control achieves the best control performance.The overshoot is almost zero and the regulation time is shorter than PID control and fuzzy PID control.