PID Ramp Controller Regulated by Fuzzy RBF Neural Network

摘要：

In this work,we apply fuzzy RBF neural network to address the traffic density control problem in a macroscopic level freeway environment with ramp metering.Firstly, a macroscopic traffic flow model to describe the freeway flow process is built.Then the architecture and function of fuzzy RBF neural network are analyzed.In conjunction with nonlinear feedback theory,a PID ramp controller regulated by fuzzy RBF neural network is designed.According to real-time traffic status,fuzzy RBF neural network is used to adjust the PID parameters dynamically in order to minimize the performance index defined in terms of the density tracking errors.Finally,the controller is simulated in MATLAB software.Simulation results show that the controller designed has good dynamic and steady-state performance,and can achieve a desired traffic density along the mainline of a freeway.This approach is quite effective to the onramp control.

关键词： freeway traffic density control ramp metering fuzzy RBF neural network PID control

作者: Tao Jiang Xinrong Liang Xinrong Liang

作者单位: College of Information Wuyi University Jiangmen Guangdong, China College of Automation South China University of Technology Guangzhou, China

会议类型: 国际会议

会议名称: 2009 Second International Conference on Intelligent Computation Technology and Automation(2009 第二届IEEE智能计算与自动化国际会议 ICICTA 2009)

会议地点: 长沙

会议语种:英文

页码: 91-94

在线出版日期: 2009-10-10（万方平台首次上网日期，不代表论文的发表时间）