Optimization of engine speed neural network PID controller based on genetic algorithm

摘要：

Engine-Dynamometer system is a two-input, dual output system with nonlinear, time-varying characteristics of large inertia,and exists coupling within the system input and output. Using the traditional PID controller, the control is often difficult to achieve the desired effectln addition, at the production site,because of being cumbersome and precision tuning effects, the traditional PID parameter tuning methods can lead to poor control of engine speed. In this paper, an engine speed neural network PID controller based on genetic algorithm which use genetic algorithm to optimizate three control parameters of neural network PID and achieving the system input and output decoupling control is studied.Simulation results show that the genetic algorithm optimizating engine speed neural network PID control system can effectively improve the accuracy ,enhance stability and fast of the system, and also have increased the engine speed control effect.

关键词： Engine speed genetic algorithm neural network PID controller decoupling control

作者: Hua-yun CAO Fu-ming PENG

作者单位: School of Automation Nanjing University of Science and Technology Nanjing,China School of Automation Nanjing University of Science and Technology Nanjing, China

会议类型: 国际会议

会议名称: 2011 Fourth International Symposium on Computational Interlligence and Design 第四届计算智能与设计国际会议 ISCID 2011

会议地点: 杭州

会议语种:英文

页码: 651-654

在线出版日期: 2011-10-28（万方平台首次上网日期，不代表论文的发表时间）