Freeway ramp metering using Fuzzy Logic and genetic proportional control

摘要

Research in the past twenty years has produced various ramp metering approaches. Even so, the results of existing ramp control techniques are unsatisfactory due to the highly nonlinear property of the ramp control system. For improving the ramp metering performance and raising the ramp control accuracy, we propose a mixed approach of fuzzy logic control and proportional plus integral (PI) control for freeway ramp metering. We also employ a genetic algorithm (GA) to optimize the PI control parameters in this paper. Furthermore, we apply a nonlinear control technique with a feedback loop to enhance the system performance. Firstly, a first-order traffic flow model called hydrodynamic model is established, and the model characteristics are analyzed. Secondly, the control objective of the ramp metering system is defined by the mainline traffic density. Thirdly, a mixed controller of fuzzy logic and genetic PI is designed based upon this hydrodynamic model and in combination with a nonlinear control technique. The membership functions of the fuzzy logic control are triangular or Gaussian curves, and the numbers of fuzzy control rules are nine. Fourthly, optimization procedures of a GA for finding the best PI control parameters are given with details. Finally, the authenticity and effectiveness of the mixed controller are verified with Matlab software R2010a and also by VISSIM microscopic traffic simulation. Simulation results prove that this mixed control approach, as well as the nonlinear control technique, is of excellent tracking performance and has tiny density errors.