In this paper, a new approach has been proposed to identify and model the dynamics of a highly maneuverable fighter aircraft through artificial neural networks（ANNs）. In general, aircraft flight dynamics is consider...In this paper, a new approach has been proposed to identify and model the dynamics of a highly maneuverable fighter aircraft through artificial neural networks（ANNs）. In general, aircraft flight dynamics is considered as a nonlinear and coupled system whose modeling through ANNs, unlike classical approaches, does not require any aerodynamic or propulsion information and a few flight test data seem sufficient. In this study, for identification and modeling of the aircraft dynamics, two known structures of internal and external recurrent neural networks（RNNs） and a proposed structure called hybrid combined recurrent neural network have been used and compared.In order to improve the training process, an appropriate evolutionary method has been applied to simultaneously train and optimize the parameters of ANNs. In this research, it has been shown that six ANNs each with three inputs and one output, trained by flight test data, can model the dynamic behavior of the highly maneuverable aircraft with acceptable accuracy and without any priori knowledge about the system.展开更多
Alternating Current(AC)charging is currently the most cost-effective and widely adopted solution for charging of Electric Vehicles(EVs).However,the existing AC charging infrastructure generally exhibits limited commun...Alternating Current(AC)charging is currently the most cost-effective and widely adopted solution for charging of Electric Vehicles(EVs).However,the existing AC charging infrastructure generally exhibits limited communication capabilities with the connected EVs,as information about the vehicle can only be collected through external logging systems that operate independently of the charger itself.A straightforward and interoperable method for extracting information from charging vehicles(e.g.,vehicle model,battery capacity,and State of Charge)could significantly enhance the implementation of advanced smart charging strategies,unlocking the flexibility of connected EVs,enabling cost reductions and supporting the provision of ancillary services to the grid.This article implements a novel machine-learning approach to estimate relevant information on AC charging vehicles in a real-world experimental setting designed and implemented by the authors.The proposed approach does not require any hardware adjustment and is capable of predicting several features of the connected EVs(e.g.,brand,model,year,battery capacity,End-of-Charge status)by exclusively considering their charging profile in response to specific prescribed current setpoints.Possible applications of the model range from the design of smart charging facilities capable of identifying regular users and forecasting their charging patterns to the real-time estimation of the aggregate flexibility of connected EVs,an essential component in vehicle-to-grid(V2G)applications.Extensive practical demonstrations based on experimental data are provided to validate the identification procedure.An example of flexibility envelope estimation of charging EVs is also included to demonstrate the potential applications of the proposed method for ancillary services provision.展开更多
文摘In this paper, a new approach has been proposed to identify and model the dynamics of a highly maneuverable fighter aircraft through artificial neural networks（ANNs）. In general, aircraft flight dynamics is considered as a nonlinear and coupled system whose modeling through ANNs, unlike classical approaches, does not require any aerodynamic or propulsion information and a few flight test data seem sufficient. In this study, for identification and modeling of the aircraft dynamics, two known structures of internal and external recurrent neural networks（RNNs） and a proposed structure called hybrid combined recurrent neural network have been used and compared.In order to improve the training process, an appropriate evolutionary method has been applied to simultaneously train and optimize the parameters of ANNs. In this research, it has been shown that six ANNs each with three inputs and one output, trained by flight test data, can model the dynamic behavior of the highly maneuverable aircraft with acceptable accuracy and without any priori knowledge about the system.
文摘Alternating Current(AC)charging is currently the most cost-effective and widely adopted solution for charging of Electric Vehicles(EVs).However,the existing AC charging infrastructure generally exhibits limited communication capabilities with the connected EVs,as information about the vehicle can only be collected through external logging systems that operate independently of the charger itself.A straightforward and interoperable method for extracting information from charging vehicles(e.g.,vehicle model,battery capacity,and State of Charge)could significantly enhance the implementation of advanced smart charging strategies,unlocking the flexibility of connected EVs,enabling cost reductions and supporting the provision of ancillary services to the grid.This article implements a novel machine-learning approach to estimate relevant information on AC charging vehicles in a real-world experimental setting designed and implemented by the authors.The proposed approach does not require any hardware adjustment and is capable of predicting several features of the connected EVs(e.g.,brand,model,year,battery capacity,End-of-Charge status)by exclusively considering their charging profile in response to specific prescribed current setpoints.Possible applications of the model range from the design of smart charging facilities capable of identifying regular users and forecasting their charging patterns to the real-time estimation of the aggregate flexibility of connected EVs,an essential component in vehicle-to-grid(V2G)applications.Extensive practical demonstrations based on experimental data are provided to validate the identification procedure.An example of flexibility envelope estimation of charging EVs is also included to demonstrate the potential applications of the proposed method for ancillary services provision.
