PID ramp controller regulated by radial basis function neural network

In this work, we apply radial basis function (RBF) neural network to address the traffic density control problem in a macroscopic level freeway environment with ramp metering. Firstly, a traffic flow model to describe the freeway flow process is established. Then an on-ramp proportional plus integral plus differential (PID) controller regulated by RBF neural network is designed. RBF neural network identifies the Jacobian matrix of the control plant and then adjusts the parameters of PID controller 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.