Disturbance observer-based control method has achieved good results in the carfollowing scenario of intelligent and connected vehicle(ICV).However,the gain of conventional extended disturbance observer(EDO)-based cont...Disturbance observer-based control method has achieved good results in the carfollowing scenario of intelligent and connected vehicle(ICV).However,the gain of conventional extended disturbance observer(EDO)-based control method is usually set manually rather than adjusted adaptively according to real time traffic conditions,thus declining the car-following performance.To solve this problem,a car-following strategy of ICV using EDO adjusted by reinforcement learning is proposed.Different from the conventional method,the gain of proposed strategy can be adjusted by reinforcement learning to improve its estimation accuracy.Since the“equivalent disturbance”can be compensated by EDO to a great extent,the disturbance rejection ability of the carfollowing method will be improved significantly.Both Lyapunov approach and numerical simulations are carried out to verify the effectiveness of the proposed method.展开更多
The increase of oil spill accidents has made significant impacts on life,property and the environment.Facing ever-increasing risk of disaster losses,how to cope with and response to large scale oil spill disaster effe...The increase of oil spill accidents has made significant impacts on life,property and the environment.Facing ever-increasing risk of disaster losses,how to cope with and response to large scale oil spill disaster effectively is becoming more and more important.And it is extremely onerous and arduous to develop a highly capable assessment technique to evaluate the effectiveness of emergency response system(ERS) for oil spill.An ERS for oil spill is a complex and dynamic system comprising a number of elements,one of which fails to accomplish its function would result in potential adverse impacts on the whole system.Evaluating the effectiveness of the system requires the consideration of all failures identified in the system simultaneously.Aims to propose a decision-making framework,this paper uses failure mode effect and criticality analysis(FMECA) to evaluate the effectiveness of ERS to make improvements in oil spill emergency management.It is achieved by analysing the components and bounds of the system,identification of generic failure modes which are considered as key factors of ERS for oil spill.And lastly a case study is demonstrated to validate the methodology framework.展开更多
基金State Key Laboratory of Automotive Safety and Energy,Grant/Award Number:KFY2208National Natural Science Foundation of China,Grant/Award Numbers:U2013601,U20A20225+1 种基金Key Research and Development Plan of Anhui Province,Grant/Award Number:202004a05020058the Natural Science Foundation of Hefei,China(Grant No.2021032)。
文摘Disturbance observer-based control method has achieved good results in the carfollowing scenario of intelligent and connected vehicle(ICV).However,the gain of conventional extended disturbance observer(EDO)-based control method is usually set manually rather than adjusted adaptively according to real time traffic conditions,thus declining the car-following performance.To solve this problem,a car-following strategy of ICV using EDO adjusted by reinforcement learning is proposed.Different from the conventional method,the gain of proposed strategy can be adjusted by reinforcement learning to improve its estimation accuracy.Since the“equivalent disturbance”can be compensated by EDO to a great extent,the disturbance rejection ability of the carfollowing method will be improved significantly.Both Lyapunov approach and numerical simulations are carried out to verify the effectiveness of the proposed method.
基金supported by the NSCF project whichis regarding "Exploring water emergency response resources allocation robust optimization of Three Gorges Reservoir Area" granted in 2012.The granted number is 51279153/E91004
文摘The increase of oil spill accidents has made significant impacts on life,property and the environment.Facing ever-increasing risk of disaster losses,how to cope with and response to large scale oil spill disaster effectively is becoming more and more important.And it is extremely onerous and arduous to develop a highly capable assessment technique to evaluate the effectiveness of emergency response system(ERS) for oil spill.An ERS for oil spill is a complex and dynamic system comprising a number of elements,one of which fails to accomplish its function would result in potential adverse impacts on the whole system.Evaluating the effectiveness of the system requires the consideration of all failures identified in the system simultaneously.Aims to propose a decision-making framework,this paper uses failure mode effect and criticality analysis(FMECA) to evaluate the effectiveness of ERS to make improvements in oil spill emergency management.It is achieved by analysing the components and bounds of the system,identification of generic failure modes which are considered as key factors of ERS for oil spill.And lastly a case study is demonstrated to validate the methodology framework.
