Open AccessAdaptive fine pollutant discharge control for motor vehicles tunnels under traffic state transition
Author(s) -
Tan Zhen,
Xia Yingjie,
Yang Qinmin,
Zhou Guomin
Publication year - 2015
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2014.0314
Subject(s) - controller (irrigation) , control theory (sociology) , aerodynamics , engineering , inertia , traffic flow (computer networking) , artificial neural network , adaptive control , control system , simulation , control engineering , computer science , control (management) , aerospace engineering , artificial intelligence , physics , computer security , electrical engineering , classical mechanics , agronomy , biology
Traffic flow dynamics is an important issue for implementing effective pollutant discharge control of tunnels. Longitudinal ventilation using jet fans is the most popular system for pollutant discharge control of tunnels. Nowadays, jet fans equipped with the frequency conversion technology in the tunnel can shorten the control cycle and even conduct manipulation of step‐less jet speeds. The longitudinal ventilation system has considerable inertia and non‐linear characteristics, which are partly resulted from traffic flow dynamics such as traffic state transition. Therefore in this study an adaptive control method based on the artificial neural‐network theory is proposed to be tailored to the traffic state transition. The model is based on aerodynamic equations and takes vehicle speed and density as main system disturbances, whose value can be determined by fundamental diagram when having incomplete field traffic data. The proposed controller can also cope with the parameters and uncertainties of the time‐varying model. The authors simulation results show that the adaptive control method can track the desirable system output effectively whenever the traffic condition changes gently or dramatically. The results also show that their method performs better than the common‐used proportional integral derivative controller in terms of system adaptability following the traffic state transition.