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This paper proposes a novel model-free adaptive control (MFAC) strategy for urban road traffic network via perimeter control based on dynamic linearization technique and predictive control. The accurate traffic flow model of the urban road network is replaced by equivalent data model. Based on the idea of predictive control, the current control action is obtained by solving online, at each sampling coordinate, a finite horizon closed-loop optimal control problem. The robustness of the MFAC strategy to time-varying desired vehicle accumulation, random traffic demand and macroscopic fundamental diagram (MFD) model uncertainty is verified through simulation results. INDEX TERMS MFAC, model predictive control (MPC), MFD, perimeter control, urban road traffic network.

An iterative learning control based on Automatic Train Operation is proposed for repetitively running train to deal with trajectory tracking problem under iteration-varying operation condition and certain speed constrains. Iteration-varying operation condition considered in this paper focuses on the air resistance coefficient of the train, which may be completely different at any two consecutive operation processes due to different weather conditions. To eliminate the influence of the resistance coefficient caused by the different weather conditions, data measured by the temperature sensors is used. In order to meet the speed constrains, the Barrier Composite Energy Function scheme is used to guarantee that the speed constraints are not violated. The effectiveness of the proposed method is verified by theoretical analysis and numerical simulation.

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