A parameter adjustment method of PID controller with BP neural network is developed and applied to freeway on-ramp metering in this paper. Firstly, the objective of ramp metering is determined, and a traffic flow model to describe the freeway flow process is built. Then the learning algorithm of BP neural network for adjusting the proportional, integral and differential coefficients is formulated in detail. Based on the traffic flow model and in conjunction with nonlinear feedback theory, an on-ramp PID controller regulated by BP neural network is designed. According to real-time traffic status, BP 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. The results show that the controller designed has good dynamic and steady-state performance. It can achieve a desired traffic density along the mainline of a freeway and thus avoid traffic congestion. This approach is quite effective to freeway on-ramp metering.
Freeway ramp PID controller regulated by BP neural network
2009 IEEE Intelligent Vehicles Symposium ; 713-717
01.06.2009
660723 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Freeway ramp PID controller regulated by BP neural network
Tema Archiv | 2009
|Freeway Ramp PID Controller Regulated by BP Neural Network
British Library Conference Proceedings | 2009
|A Local Neural Network Controller for Freeway Ramp Metering
British Library Conference Proceedings | 1995
|PID ramp controller regulated by fuzzy RBF neural network
Tema Archiv | 2009
|Freeway ramp metering using artificial neural networks
Online Contents | 1997
|