This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mi...This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.展开更多
This study presents an improved data-driven Model-Free Adaptive Control(MFAC)strategy for attitude stabilization of a partially constrained combined spacecraft with external disturbances and input saturation. First, a...This study presents an improved data-driven Model-Free Adaptive Control(MFAC)strategy for attitude stabilization of a partially constrained combined spacecraft with external disturbances and input saturation. First, a novel dynamic linearization data model for the partially constrained combined spacecraft with external disturbances is established. The generalized disturbances composed of external disturbances and dynamic linearization errors are then reconstructed by a Discrete Extended State Observer(DESO). With the dynamic linearization data model and reconstructed information, a DESO-MFAC strategy for the combined spacecraft is proposed based only on input and output data. Next, the input saturation is overcome by introducing an antiwindup compensator. Finally, numerical simulations are carried out to demonstrate the effectiveness and feasibility of the proposed controller when the dynamic properties of the partially constrained combined spacecraft are completely unknown.展开更多
Climate change is accelerating globally,raising significant concerns regarding the environmental risks associated with combined sewer overflows(CSOs).These rainfall events lead to the excessive discharge of multiple p...Climate change is accelerating globally,raising significant concerns regarding the environmental risks associated with combined sewer overflows(CSOs).These rainfall events lead to the excessive discharge of multiple pollutants into natural waters.However,greenhouse gas(GHG)emissions from CSOs,which are crucial for carbon neutrality in urban water systems,remain fragmented.Using the life-cycle assess-ment method expansion approach,this study breaks down the formation and discharge processes of CSOs and uncovers the underlying mechanisms driving GHG emissions during each period.Given the complex-ity and uncertainty in the spatial distribution of GHG emissions from CSOs,the development of standard monitoring and estimation methods is vital.This study identifies the factors influencing GHG emissions within the urban drainage system(UDS)and defines the interactive GHG emission boundaries and accounting framework related to CSOs.This framework is expanded to consider the hybrid nature of urban engineering and hydraulic interactions during the CSO events.Advanced modeling technologies have emerged as essential tools for predicting and managing GHG emissions from CSOs.This review pro-motes comprehensive data-driven methods for predicting GHG emissions from CSOs,fully considering the inherent heterogeneity of CSOs and the impact of multi-source contaminants discharged into aquatic environments.It emphasizes refining emission boundary definitions,novel accounting practices adapting data-driven methods,and comprehensive management strategies in line with the move toward carbon neutrality in the UDS.It advocates the adoption of solutions including advanced technologies and artifi-cial intelligent methods to mitigate CSO-related GHG emissions,stressing the significance of integrating low-carbon solutions and a comprehensive data-driven management framework in future research directions.展开更多
The real-time model-based control of polymer electrolyte membrane(PEM)fuel cells requires a computationally efficient and sufficiently accurate model to predict the transient and long-term performance under various op...The real-time model-based control of polymer electrolyte membrane(PEM)fuel cells requires a computationally efficient and sufficiently accurate model to predict the transient and long-term performance under various operational conditions,involving the pressure,temperature,humidity,and stoichiometry ratio.In this article,recent progress on the development of PEM fuel cell models that can be used for real-time control is reviewed.The major operational principles of PEM fuel cells and the associated mathematical description of the transport and electrochemical phenomena are described.The reduced-dimensional physics-based models(pseudo-twodimensional,one-dimensional numerical and zero dimensional analytical models)and the non-physics-based models(zero-dimensional empirical and data-driven models)have been systematically examined,and the comparison of these models has been performed.It is found that the current trends for the real-time control models are(i)to couple the single cell model with balance of plants to investigate the system performance,(ii)to incorporate aging effects to enable long-term performance prediction,(iii)to increase the computational speed(especially for one-dimensional numerical models),and(iv)to develop data-driven models with artificial intelligence/machine learning algorithms.This review will be beneficial for the development of physics or nonphysics based models with sufficient accuracy and computational speed to ensure the real-time control of PEM fuel cells.展开更多
The organized three-dimensional chromosome architecture in the cell nucleus provides scaffolding for precise regulation of gene expression.When the cell changes its identity in the cell-fate decision-making process,ex...The organized three-dimensional chromosome architecture in the cell nucleus provides scaffolding for precise regulation of gene expression.When the cell changes its identity in the cell-fate decision-making process,extensive rearrangements of chromo-some structures occur accompanied by large-scale adaptations of gene expression,underscoring the importance of chromosome dynamics in shaping genome function.Over the last two decades,rapid development of experimental methods has provided unprecedented data to characterize the hierarchical structures and dynamic properties of chromosomes.In parallel,these enormous data offer valuable opportunities for developing quantitative computational models.Here,we review a variety of large-scale polymer models developed to investigate the structures and dynamics of chromosomes.Different from the underlying modeling strategies,these approaches can be classified into data-driven(‘top-down’)and physics-based(‘bottom-up’)categories.We discuss their contributions to offering valuable insights into the relationships among the structures,dynamics,and functions of chromosomes and propose the perspective of developing data integration approaches from different experimental technologies and multidisciplinary theoretical/simulation methods combined with different modeling strategies.展开更多
A combined system including a solid oxide fuel cell(SOFC)and an internal combustion engine(ICE)is proposed in this paper.First,a 0-D model of SOFC and a 1-D model of ICE are built as agent models.Second,parameter anal...A combined system including a solid oxide fuel cell(SOFC)and an internal combustion engine(ICE)is proposed in this paper.First,a 0-D model of SOFC and a 1-D model of ICE are built as agent models.Second,parameter analysis of the system is conducted based on SOFC and ICE models.Results show that the number of cells,current density,and fuel utilization can influence SOFC and ICE.Moreover,a deep neural network is applied as a data-driven model to conduct optimized calculations efficiently,as achieved by the particle swarm optimization algorithm in this paper.The results demonstrate that the optimal system efficiency of 51.8%can be achieved from a 22.4%/77.6%SOFC-ICE power split at 6000 kW power output.Furthermore,promising improvements in efficiency of 5.1%are achieved compared to the original engine.Finally,a simple economic analysis model,which shows that the payback period of the optimal system is 8.41 years,is proposed in this paper.展开更多
In this study,a novel application of the Koopman operator for control-oriented modeling of proton exchange membrane fuel cell(PEMFC)stacks is proposed.The primary contributions of this paper are:(1)the design of Koopm...In this study,a novel application of the Koopman operator for control-oriented modeling of proton exchange membrane fuel cell(PEMFC)stacks is proposed.The primary contributions of this paper are:(1)the design of Koopman-based models for a fuel cell stack,incorporating K-fold cross-validation,varying lifted dimensions,radial basis functions(RBFs),and prediction horizons;and(2)comparison of the performance of Koopman-based approach with a more traditional physics-based model.The results demonstrate the high accuracy of the Koopman-based model in predicting fuel cell stack behavior,with an error of less than 3%.The proposed approach offers several advantages,including enhanced computational efficiency,reduced computational burden,and improved interpretability.This study demonstrates the suitability of the Koopman operator for the modeling and control of PEMFCs and provides valuable insights into a novel control-oriented modeling approach that enables accurate and efficient predictions for fuel cell stacks.展开更多
The rapid development of digital technolo-gy has fundamentally changed the ways we live,work,and study.Digital education has gradually emerged under the influence of social change,technological advancements,global com...The rapid development of digital technolo-gy has fundamentally changed the ways we live,work,and study.Digital education has gradually emerged under the influence of social change,technological advancements,global competition,and innovative ed-ucational practice.Digital education is not just a sim-ple application of digital technology in education but a new educational paradigm.It builds a more equitable,higher-quality,environmentally friendly,and openly cooperative new education system through data-driven methods,human-technology integration,the combi-nation of virtual and real elements,and open sharing.Developing digital education involves focusing on sce-narios,resources,models,evaluation,and digital litera-cy.China has made significant progress in developing digital education,accumulating valuable experience that can inform the continued and prosperous growth of digital education worldwide.While acknowledg-ing the advantages that digitalization brings to teach-ing,evaluation,and management,we also need to be aware of the risks and challenges it brings to data secu-rity,privacy protection,ethical issues,and humanistic concerns.展开更多
To manage a large amount of flexible distributed energy resources(DERs)in the distribution networks,the virtual power plant(VPP)is introduced into the industry.The VPP can optimally dispatch these resources in a clust...To manage a large amount of flexible distributed energy resources(DERs)in the distribution networks,the virtual power plant(VPP)is introduced into the industry.The VPP can optimally dispatch these resources in a cluster manner and provide flexibility for the power system operation as a whole.Most existing studies formulate the equivalent power flexibility of the aggregating DERs as deterministic optimization models without considering their uncertainties.In this paper,we introduce the stochastic power flexibility range(PFR)and timecoupling flexibility(TCF)to describe the power flexibility of VPP.In this model,both operational constraints and the randomness of the DERs’output are incorporated,and a combined model and data-driven solution is proposed to obtain the stochastic PFR,TCF,and cost function of VPP.The aggregating model can be easily incorporated into the optimization model for the power system operator or market bidding,considering uncertainties.Finally,a numerical test is performed.The results show that the proposed model not only has higher computational efficiency than the scenario-based methods but also achieves more economic benefits.展开更多
基金supported by the National Natural Science Foundation of China(No.12372045)the National Key Research and the Development Program of China(Nos.2023YFC2205900,2023YFC2205901)。
文摘This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.
基金supported by National Natural Science Foundation of China(Nos.61603114,61673135)the Fundamental Research Funds for the Central Universities of China(No.HIT.NSRIF.201826)
文摘This study presents an improved data-driven Model-Free Adaptive Control(MFAC)strategy for attitude stabilization of a partially constrained combined spacecraft with external disturbances and input saturation. First, a novel dynamic linearization data model for the partially constrained combined spacecraft with external disturbances is established. The generalized disturbances composed of external disturbances and dynamic linearization errors are then reconstructed by a Discrete Extended State Observer(DESO). With the dynamic linearization data model and reconstructed information, a DESO-MFAC strategy for the combined spacecraft is proposed based only on input and output data. Next, the input saturation is overcome by introducing an antiwindup compensator. Finally, numerical simulations are carried out to demonstrate the effectiveness and feasibility of the proposed controller when the dynamic properties of the partially constrained combined spacecraft are completely unknown.
基金supported by the National Natural Science Foun-dation of China(52325001,52170009,and 52400114)the National Key Research and Development Program of China(2021YFC3200700 and 2021YFC3200702)+1 种基金the Program of Shanghai Academic Research Leader,China(21XD1424000)the Fundamental Research Funds for the Central Universities.
文摘Climate change is accelerating globally,raising significant concerns regarding the environmental risks associated with combined sewer overflows(CSOs).These rainfall events lead to the excessive discharge of multiple pollutants into natural waters.However,greenhouse gas(GHG)emissions from CSOs,which are crucial for carbon neutrality in urban water systems,remain fragmented.Using the life-cycle assess-ment method expansion approach,this study breaks down the formation and discharge processes of CSOs and uncovers the underlying mechanisms driving GHG emissions during each period.Given the complex-ity and uncertainty in the spatial distribution of GHG emissions from CSOs,the development of standard monitoring and estimation methods is vital.This study identifies the factors influencing GHG emissions within the urban drainage system(UDS)and defines the interactive GHG emission boundaries and accounting framework related to CSOs.This framework is expanded to consider the hybrid nature of urban engineering and hydraulic interactions during the CSO events.Advanced modeling technologies have emerged as essential tools for predicting and managing GHG emissions from CSOs.This review pro-motes comprehensive data-driven methods for predicting GHG emissions from CSOs,fully considering the inherent heterogeneity of CSOs and the impact of multi-source contaminants discharged into aquatic environments.It emphasizes refining emission boundary definitions,novel accounting practices adapting data-driven methods,and comprehensive management strategies in line with the move toward carbon neutrality in the UDS.It advocates the adoption of solutions including advanced technologies and artifi-cial intelligent methods to mitigate CSO-related GHG emissions,stressing the significance of integrating low-carbon solutions and a comprehensive data-driven management framework in future research directions.
基金This work received financial support from Toyota Motor Engineering&Manufacturing North America,Inc.,Toyota Motor Manufacturing Canada,and Natural Sciences and Engineering Research Council of Canada through a Collaborative Research and Development Grant with the project number of CRDPJ 543945-19.
文摘The real-time model-based control of polymer electrolyte membrane(PEM)fuel cells requires a computationally efficient and sufficiently accurate model to predict the transient and long-term performance under various operational conditions,involving the pressure,temperature,humidity,and stoichiometry ratio.In this article,recent progress on the development of PEM fuel cell models that can be used for real-time control is reviewed.The major operational principles of PEM fuel cells and the associated mathematical description of the transport and electrochemical phenomena are described.The reduced-dimensional physics-based models(pseudo-twodimensional,one-dimensional numerical and zero dimensional analytical models)and the non-physics-based models(zero-dimensional empirical and data-driven models)have been systematically examined,and the comparison of these models has been performed.It is found that the current trends for the real-time control models are(i)to couple the single cell model with balance of plants to investigate the system performance,(ii)to incorporate aging effects to enable long-term performance prediction,(iii)to increase the computational speed(especially for one-dimensional numerical models),and(iv)to develop data-driven models with artificial intelligence/machine learning algorithms.This review will be beneficial for the development of physics or nonphysics based models with sufficient accuracy and computational speed to ensure the real-time control of PEM fuel cells.
基金supported by the National Natural Science Foundation of China(grant no.32201020)the general program(grant no.2023A04J0083)+1 种基金the Guangzhou–HKUST(GZ)joint funding program(grant no.2023A03J0060)of Guangzhou Municipal Science and Technology Projectfunded by the Municipal Key Laboratory Construction Program of Guangzhou Municipal Science and Technology Project(grant no.2023A03J0003).
文摘The organized three-dimensional chromosome architecture in the cell nucleus provides scaffolding for precise regulation of gene expression.When the cell changes its identity in the cell-fate decision-making process,extensive rearrangements of chromo-some structures occur accompanied by large-scale adaptations of gene expression,underscoring the importance of chromosome dynamics in shaping genome function.Over the last two decades,rapid development of experimental methods has provided unprecedented data to characterize the hierarchical structures and dynamic properties of chromosomes.In parallel,these enormous data offer valuable opportunities for developing quantitative computational models.Here,we review a variety of large-scale polymer models developed to investigate the structures and dynamics of chromosomes.Different from the underlying modeling strategies,these approaches can be classified into data-driven(‘top-down’)and physics-based(‘bottom-up’)categories.We discuss their contributions to offering valuable insights into the relationships among the structures,dynamics,and functions of chromosomes and propose the perspective of developing data integration approaches from different experimental technologies and multidisciplinary theoretical/simulation methods combined with different modeling strategies.
文摘A combined system including a solid oxide fuel cell(SOFC)and an internal combustion engine(ICE)is proposed in this paper.First,a 0-D model of SOFC and a 1-D model of ICE are built as agent models.Second,parameter analysis of the system is conducted based on SOFC and ICE models.Results show that the number of cells,current density,and fuel utilization can influence SOFC and ICE.Moreover,a deep neural network is applied as a data-driven model to conduct optimized calculations efficiently,as achieved by the particle swarm optimization algorithm in this paper.The results demonstrate that the optimal system efficiency of 51.8%can be achieved from a 22.4%/77.6%SOFC-ICE power split at 6000 kW power output.Furthermore,promising improvements in efficiency of 5.1%are achieved compared to the original engine.Finally,a simple economic analysis model,which shows that the payback period of the optimal system is 8.41 years,is proposed in this paper.
基金This material is based upon work supported by the National Science Foundation,United States under Grant No.2135735.
文摘In this study,a novel application of the Koopman operator for control-oriented modeling of proton exchange membrane fuel cell(PEMFC)stacks is proposed.The primary contributions of this paper are:(1)the design of Koopman-based models for a fuel cell stack,incorporating K-fold cross-validation,varying lifted dimensions,radial basis functions(RBFs),and prediction horizons;and(2)comparison of the performance of Koopman-based approach with a more traditional physics-based model.The results demonstrate the high accuracy of the Koopman-based model in predicting fuel cell stack behavior,with an error of less than 3%.The proposed approach offers several advantages,including enhanced computational efficiency,reduced computational burden,and improved interpretability.This study demonstrates the suitability of the Koopman operator for the modeling and control of PEMFCs and provides valuable insights into a novel control-oriented modeling approach that enables accurate and efficient predictions for fuel cell stacks.
基金supported by“An International Comparative Study on the Digital Transformation of Education”,a Major Program of the National Social Science Fund of the Ministry of Education of the People’s Republic of China for the year 2022(No.22JZD045).
文摘The rapid development of digital technolo-gy has fundamentally changed the ways we live,work,and study.Digital education has gradually emerged under the influence of social change,technological advancements,global competition,and innovative ed-ucational practice.Digital education is not just a sim-ple application of digital technology in education but a new educational paradigm.It builds a more equitable,higher-quality,environmentally friendly,and openly cooperative new education system through data-driven methods,human-technology integration,the combi-nation of virtual and real elements,and open sharing.Developing digital education involves focusing on sce-narios,resources,models,evaluation,and digital litera-cy.China has made significant progress in developing digital education,accumulating valuable experience that can inform the continued and prosperous growth of digital education worldwide.While acknowledg-ing the advantages that digitalization brings to teach-ing,evaluation,and management,we also need to be aware of the risks and challenges it brings to data secu-rity,privacy protection,ethical issues,and humanistic concerns.
基金supported in part by the National Natural Science Foundation of China under Grant U2066601,51725703Southern Power Grid Technical Project GDKJXM20185069(032000KK52180069).
文摘To manage a large amount of flexible distributed energy resources(DERs)in the distribution networks,the virtual power plant(VPP)is introduced into the industry.The VPP can optimally dispatch these resources in a cluster manner and provide flexibility for the power system operation as a whole.Most existing studies formulate the equivalent power flexibility of the aggregating DERs as deterministic optimization models without considering their uncertainties.In this paper,we introduce the stochastic power flexibility range(PFR)and timecoupling flexibility(TCF)to describe the power flexibility of VPP.In this model,both operational constraints and the randomness of the DERs’output are incorporated,and a combined model and data-driven solution is proposed to obtain the stochastic PFR,TCF,and cost function of VPP.The aggregating model can be easily incorporated into the optimization model for the power system operator or market bidding,considering uncertainties.Finally,a numerical test is performed.The results show that the proposed model not only has higher computational efficiency than the scenario-based methods but also achieves more economic benefits.
