PID Ramp Controller Regulated by Fuzzy RBF Neural Network

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 on-ramp control.