Modified iterative-learning-control-based ramp metering strategies for freeway traffic control with iteration-dependent fctors
AffiliationBeijing Jiaotong University, Beijing, China
freeway traffic control
iterative learning control (ILC)
iterative learning control
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AbstractFor a freeway traffic system with strict repeatable pattern, iterative learning control (ILC) has been successfully applied to local ramp metering for a macroscopic freeway environment by formulating the original ramp metering problem as an output tracking, disturbance rejection, and error compensation problem. In this paper, we address the freeway traffic ramp-metering system under a nonstrict repeatable pattern. ILC-based ramp metering and ILC add-on to ALINEA strategies are modified to deal with the presence of iteration-dependent parameters, iteration-dependent desired trajectory, and input constraints. Theoretical analysis and extensive simulations are used to verify the effectiveness of the proposed approaches.
CitationHou, Z., Yan, J., Xu, J.-X., Li, Z. (2012) 'Modified Iterative-Learning-Control-Based Ramp Metering Strategies for Freeway Traffic Control With Iteration-Dependent Factors', IEEE Transactions on Intelligent Transportation Systems 13 (2):606-618
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Iterative learning control with unknown control direction: a novel data-based approachShen, Dong; Hou, Zhongsheng; Chinese Academy of Sciences (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2011)Iterative learning control (ILC) is considered for both deterministic and stochastic systems with unknown control direction. To deal with the unknown control direction, a novel switching mechanism, based only on available system tracking error data, is first proposed. Then two ILC algorithms combined with the novel switching mechanism are designed for both deterministic and stochastic systems. It is proved that the ILC algorithms would switch to the right control direction and stick to it after a finite number of cycles. Moreover, the input sequence converges to the desired one under the deterministic case. The input sequence converges to the optimal one with probability 1 under stochastic case and the resulting tracking error tends to its minimal value.
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