Efficient predictive model-based and fuzzy control for green urban mobility

In this thesis, we develop efficient predictive model-based control approaches, including model-predictive control (MPC) andmodel-based fuzzy control, for application in urban traffic networks with the aim of reducing a combination of the total time spent by the vehicles within the network and the total emissions. The thesis includes three main parts, where in the first part the main focus is on accurate approaches for estimating the macroscopic traffic variables, such as the temporal-spatial averages, from a microscopic point-of-view. The second part includes efficient approaches for solving the optimization problem of the nonlinear MPC controller. The third and last part of the thesis proposes an adaptive and predictivemodel-based type-2 fuzzy control scheme that can be implementedwithin amulti-agent control architecture.

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