OPTIMAL NEURAL FEEDBACK CONTROL FOR CARBON TAX POLICY GAUGING IN TRANSPORTATION

摘要

The effects of carbon emissions have been the objective of an investigation that was based on the model of the nation-wide transportation system with railway, waterway, and roadway. The dynamics of such a complex phenomenon depends on a set of control variables (i.e., the percentage of carbon tax on the fuel cost, the operational cost coverages, and growth rates of the various transportation modes) that can be chosen in a suitable way so as to minimize a given cost function (e.g., carbon emissions, public and private costs, fuel consumption, etc.). This problem has been addressed by searching for a feedback control law that can be approximated by means of the combination of both Dynamic Programming and neural networks. Preliminary simulation results with the afore-mentioned model are presented to demonstrate the effectiveness of the proposed method.