A data-driven control design approach for freeway traffic ramp metering with virtual reference feedback tuning. ALINEA is a simple, efficient, and easily implemented ramp metering strategy. Virtual reference feedback tuning (VRFT) is most suitable for many practical systems since it is a “one-shot” data-driven control design methodology. This paper presents an application of VRFT to a ramp metering problem of freeway traffic system. When there is not enough prior knowledge of the controlled system to select a proper parameter of ALINEA, the VRFT approach is used to optimize the ALINEA’s parameter by only using a batch of input and output data collected from the freeway traffic system. The extensive simulations are built on both the macroscopic MATLAB platform and the microscopic PARAMICS platform to show the effectiveness and applicability of the proposed data-driven controller tuning approach.
References in zbMATH (referenced in 2 articles , 1 standard article )
Showing results 1 to 2 of 2.
- Wang, Hongbin; Dong, Jian; Wang, Yueling: High-order feedback iterative learning control algorithm with forgetting factor (2015)
- Jin, Shangtai; Hou, Zhongsheng; Chi, Ronghu; Hao, Jiangen: A data-driven control design approach for freeway traffic ramp metering with virtual reference feedback tuning (2014)