This paper presents an application of adaptive neural network model-based predictive control (MPC) to the air-fuel ratio of an engine simulation. A multi-layer perceptron (MLP) neural network is trained using two on-l...This paper presents an application of adaptive neural network model-based predictive control (MPC) to the air-fuel ratio of an engine simulation. A multi-layer perceptron (MLP) neural network is trained using two on-line training algorithms: a back propagation algorithm and a recursive least squares (RLS) algorithm. It is used to model parameter uncertainties in the nonlinear dynamics of internal combustion (IC) engines. Based on the adaptive model, an MPC strategy for controlling air-fuel ratio is realized, and its control performance compared with that of a traditional PI controller. A reduced Hessian method, a newly developed sequential quadratic programming (SQP) method for solving nonlinear programming (NLP) problems, is implemented to speed up nonlinear optimization in the MPC. Keywords Air-fuel ratio control - IC engine - adaptive neural networks - nonlinear programming - model predictive control Shi-Wei Wang PhD student, Liverpool John Moores University; MSc in Control Systems, University of Sheffield, 2003; BEng in Automatic Technology, Jilin University, 2000; Current research interests automotive engine control, model predictive control, sliding mode control, neural networks.Ding-Li Yu obtained B.Eng from Harbin Civil Engineering College, Harbin, China in 1981, M.Sc from Jilin University of Technology, Changchun, China in 1986 and PhD from Coventry University, U.K. in 1995, all in control engineering. He is currently a Reader in Process Control at Liverpool John Moores University, U.K. His current research interests are in process control, engine control, fault detection and adaptive neural nets. He is a member of SAFEPROCESS TC in IFAC and an associate editor of the IJMIC and the IJISS.展开更多
The fuzzy neural networks has been used as means of precisely controlling the air-fuel ratio of a lean-burn compressed natural gas (CNG) engine. A control algorithm, without based on engine model, has been (utilized) ...The fuzzy neural networks has been used as means of precisely controlling the air-fuel ratio of a lean-burn compressed natural gas (CNG) engine. A control algorithm, without based on engine model, has been (utilized) to construct a feedforward/feedback control scheme to regulate the air-fuel ratio. Using fuzzy neural networks, a fuzzy neural hybrid controller is obtained based on PI controller. The new controller, which can adjust parameters online, has been tested in transient air-fuel ratio control of a CNG engine.展开更多
An aerosol electrical mobility spectrum analyzer(AEMSA),developed at Hanyang University,was employed to investigate the particle charge characteristics in the Antarctic and Arctic regions.The particle charge character...An aerosol electrical mobility spectrum analyzer(AEMSA),developed at Hanyang University,was employed to investigate the particle charge characteristics in the Antarctic and Arctic regions.The particle charge characteristics in these areas were compared with the charging state in Ansan,South Korea,located in the midlatitude,where artificial factors,such as human activity,urbanization,and traffic,might result in a higher total concentration.Furthermore,in Ansan,South Korea,the charged-particle polarity ratio was very stable and was close to 1.However,notably different particle charge characteristics were obtained in the Antarctic and Arctic regions.The imbalance between the numbers of positively and negatively charged particles was evident,resulting in more positive charges on the atmospheric particles.On average,the positively charged particle concentrations in the Antarctic and Arctic areas were 1.4 and 2.8 times higher,respectively,compared with the negatively charged particles.The developed AEMSA system and the findings of this study provide useful information on the characteristics of atmospheric aerosols in the Antarctic and Arctic regions and can be further utilized to study particle formation mechanisms.展开更多
文摘This paper presents an application of adaptive neural network model-based predictive control (MPC) to the air-fuel ratio of an engine simulation. A multi-layer perceptron (MLP) neural network is trained using two on-line training algorithms: a back propagation algorithm and a recursive least squares (RLS) algorithm. It is used to model parameter uncertainties in the nonlinear dynamics of internal combustion (IC) engines. Based on the adaptive model, an MPC strategy for controlling air-fuel ratio is realized, and its control performance compared with that of a traditional PI controller. A reduced Hessian method, a newly developed sequential quadratic programming (SQP) method for solving nonlinear programming (NLP) problems, is implemented to speed up nonlinear optimization in the MPC. Keywords Air-fuel ratio control - IC engine - adaptive neural networks - nonlinear programming - model predictive control Shi-Wei Wang PhD student, Liverpool John Moores University; MSc in Control Systems, University of Sheffield, 2003; BEng in Automatic Technology, Jilin University, 2000; Current research interests automotive engine control, model predictive control, sliding mode control, neural networks.Ding-Li Yu obtained B.Eng from Harbin Civil Engineering College, Harbin, China in 1981, M.Sc from Jilin University of Technology, Changchun, China in 1986 and PhD from Coventry University, U.K. in 1995, all in control engineering. He is currently a Reader in Process Control at Liverpool John Moores University, U.K. His current research interests are in process control, engine control, fault detection and adaptive neural nets. He is a member of SAFEPROCESS TC in IFAC and an associate editor of the IJMIC and the IJISS.
文摘The fuzzy neural networks has been used as means of precisely controlling the air-fuel ratio of a lean-burn compressed natural gas (CNG) engine. A control algorithm, without based on engine model, has been (utilized) to construct a feedforward/feedback control scheme to regulate the air-fuel ratio. Using fuzzy neural networks, a fuzzy neural hybrid controller is obtained based on PI controller. The new controller, which can adjust parameters online, has been tested in transient air-fuel ratio control of a CNG engine.
基金supported by the research fund of Hanyang University(HY-2019-P).
文摘An aerosol electrical mobility spectrum analyzer(AEMSA),developed at Hanyang University,was employed to investigate the particle charge characteristics in the Antarctic and Arctic regions.The particle charge characteristics in these areas were compared with the charging state in Ansan,South Korea,located in the midlatitude,where artificial factors,such as human activity,urbanization,and traffic,might result in a higher total concentration.Furthermore,in Ansan,South Korea,the charged-particle polarity ratio was very stable and was close to 1.However,notably different particle charge characteristics were obtained in the Antarctic and Arctic regions.The imbalance between the numbers of positively and negatively charged particles was evident,resulting in more positive charges on the atmospheric particles.On average,the positively charged particle concentrations in the Antarctic and Arctic areas were 1.4 and 2.8 times higher,respectively,compared with the negatively charged particles.The developed AEMSA system and the findings of this study provide useful information on the characteristics of atmospheric aerosols in the Antarctic and Arctic regions and can be further utilized to study particle formation mechanisms.
