Iterative Learning Perimeter Control For Urban Traffic Region

With the rapid growth of car ownership,many large cities are facing regional congestion problems,which causes serious impact on social life and national economy.Therefore,the optimal control of urban traffic system is an essential scientific issue and an important research direction in intelligent transportation field.In recent years,many domestic and foreign scholars begin to use Macroscopic Fundamental Diagram(MFD)to design the perimeter controller to regulate the traffic flow from the macroscopic level and maximize the road network capacity.At present,MFD-based methods are mostly based on model-based feedback control algorithm.However,the model parameters of the actual road network are difficult to be estimated and susceptible to environmental impact,which affects the practical application of these control algorithms.With the rapid development of information,sensing and computer technology,the urban traffic system collects and stores massive data which contain the running state of the transport system at all times.In addition,the daily or weekly or even yearly operation of urban transport system has repetitive nature.Iterative Learning Control(ILC)makes full use of the system repetition information to design the controller,so that the system control performance can be improved with the increase of the running times.Therefore,based on the repetitive characteristics of urban traffic flow,this thesis proposes two iterative learning perimeter control schemes for urban traffic region.The corresponding error convergence analysis is given and the simulation results show that the iterative learning control method can achieve the desired control performancefor the urban traffic region under various scenarios.The main work is as follows:Firstly,the background,basic properties,influencing factors and the formula for calculating the relevant parameters of MFD are introduced.Urban transportation system model and relative analysis based on MFD are given.Secondly,based on the existing research results of MFD and ILC,an iterative learning perimeter control scheme for urban traffic region is proposed,and the error convergence analysis is given.The simulation analysis of different road network conditions(morning and evening peak,congested central region,time-varying traffic demand,different expected number of vehicles and different MFD)are conducted from the macroscopic perspective.The results show that the iterative learning perimeter control scheme has a good control effect under various scenarios when the system parameter changes only in the time axis.Thirdly,in order to restrain the iterative change of the traffic system parameters,a feedforward feedback iterative learning control scheme for the urban traffic region is proposed,and the error convergence proof is given.To compare with the iterative learning boundary control scheme,the simulation analysis of different road network conditions(morning and evening peak,congested central region,and different expected number of vehicles)are conducted.The results show that the feedforward feedback iterative learning controller can achieve fast convergence of the error when the system parameters change along the time axis and iteration axis,and ensure the high accurate tracking and good anti-disturbance ability.