Radial basis function neural network-based model predictive control for freeway traffic systems

Authors:
Wang Dongli;Zhou Yan;He Xiaoyang
Affiliations:
Glorious Sun School of Business Administration, Donghua University, Shanghai 20051, China.;Department of Automation, Shanghai Jiaotong University, Shanghai 200240, China.;College of Electric Engineering, Guangxi University, Nanning 530004, China
Venue:
International Journal of Intelligent Systems Technologies and Applications
Year:
2007

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Abstract

A method of Radial Basis Function neural network-based Model Predictive Control (RBF-MPC) for freeway traffic systems is proposed in this paper. Because of nonlinearity and uncertainty of freeway traffic flow, an accurate mathematics model cannot be obtained. Therefore, RBF neural networks employed to predict the future behaviours of freeway traffic flow are designed based on the MATLAB neural network toolbox. Then, to handle nonlinearity, time delay, uncertainty and strong disturbance, RBF-MPC for ramp metering is proposed. A Genetic Algorithm (GA) is used in the receding horizon optimisation. Compared with the no-control case and optimal-control case, the simulation results demonstrate that the proposed approach can alleviate traffic jams and increase main road capacity; thus the efficiency of freeway traffic is improved tremendously.