Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainti...Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.展开更多
This study addresses a new charging station network planning problem for smart connected electric vehicles.We embed a charging station choice model into a charging network planning model that explicitly considers the ...This study addresses a new charging station network planning problem for smart connected electric vehicles.We embed a charging station choice model into a charging network planning model that explicitly considers the heterogeneity of the charging behavior in a data-driven manner.To cope with the deficiencies from a small size and sparse behavioral data,we propose a robust charging demand prediction method that can significantly reduce the impact of sample errors and missing data.On the basis of these two building blocks,we form and solve a new optimal charging station location and capacity problem by minimizing the construction and charging costs while considering the charging service level,construction budget,and limit to the number of chargers.We use a case study of planning charging stations in Shanghai to validate our contributions and provide managerial insight in this area.展开更多
Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model...Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model approximating the actual system is obtained online.The upper bound of the discrepancy between the identified model and the actual system is estimated using real-time prediction error,which is then utilized in the design of a tube-based robust model predictive controller.The effectiveness of the proposed approach is validated by numerical simulation.展开更多
To solve the attitude trajectory tracking problem for hypersonic vehicles in the presence of system constraints and unknown disturbances,this paper designed a nonlinear robust model predictive control(RMPC)scheme,whic...To solve the attitude trajectory tracking problem for hypersonic vehicles in the presence of system constraints and unknown disturbances,this paper designed a nonlinear robust model predictive control(RMPC)scheme,which can produce near-optimal tracking commands.Unlike the existing designs,the proposed scheme is less conservative and successfully prioritizes the solution optimality.The established RMPC follows a dualloop structure.Specifically,in the outer feedback loop,the reference attitude angle profiles are optimally tracked,while in the inner feedback loop,the control moment commands are produced by optimally tracking the desired angular rate trajectories.Besides,an adaptive disturbance observer(ADO)is designed and embedded in the inner and outer RMPC controllers to alleviate the negative effects caused by unknown external disturbances.The recursive feasibility of the optimization process,together with the input-to-state stability of the proposed RMPC,is theoretically guaranteed by introducing a tightened control constraint and terminal region.The derived property reveals that our proposal can steer the tracking error within a small region of convergence.Finally,the effectiveness of the proposed scheme is demonstrated by performing simulation studies.展开更多
For a class of linear discrete-time systems that is subject to randomly occurred networked packet loss in industrial cyber physical systems, a novel robust model predictive control method with active compensation mech...For a class of linear discrete-time systems that is subject to randomly occurred networked packet loss in industrial cyber physical systems, a novel robust model predictive control method with active compensation mechanism was proposed. The probability distribution of packet loss is described as the Bernoulli distributed white sequences. By using the Lyapunov stability theory, the existing sufficient conditions of the controller are derived from solving a group of linear matrix inequalities. Moreover, dropout-rate with uncertainty and unknown dropout-rate are also considered, which can greatly reduce the conservativeness of the controller. The designed robust model predictive control method not only efficiently eliminates the negative effects of the networked data loss in industrial cyber physical systems but also ensures the stability of closed-loop system. Two examples were provided to illustrate the superiority and effectiveness of the proposed method.展开更多
In this paper, a robust model predictive control approach is proposed for a class of uncertain systems with time-varying, linear fractional transformation perturbations. By adopting a sequence of feedback control laws...In this paper, a robust model predictive control approach is proposed for a class of uncertain systems with time-varying, linear fractional transformation perturbations. By adopting a sequence of feedback control laws instead of a single one, the control performance can be improved and the region of attraction can be enlarged compared with the existing model predictive control （MPC） approaches. Moreover, a synthesis approach of MPC is developed to achieve high performance with lower on-line computational burden. The effectiveness of the proposed approach is verified by simulation examples.展开更多
Recently,heavy precipitation(HP)events have occurred frequently in North China(NC),causing devastating economic losses and human fatalities.However,the short-term climate prediction of HP is quite limited.Combining ye...Recently,heavy precipitation(HP)events have occurred frequently in North China(NC),causing devastating economic losses and human fatalities.However,the short-term climate prediction of HP is quite limited.Combining year-to-year increment(DY)method and sliding correlations,we developed a robust seasonal prediction model for late-summer HP days(HPDs)in NC during 1982–2022,utilizing three independent predictors—February sea surface temperature(SST)in the Indian Ocean(SST_IO),February snow depth over North Asia(SDE_NA),and May melted snow depth in NC(MSDE_NC).The SST_IO anomalies affect NC's precipitation through the Pacific-Japan pattern.The SDE_NA anomalies are associated with East Asian anomalous anticyclone by southeastern propagation of Rossby wave at Eurasia.The MSDE_NC anomalies are followed by vertical motion and moisture anomalies in situ and thereby cause precipitation anomalies.This prediction model can well simulate the variations of the HPDs,with a correlation coefficient(CC)of 0.81(0.65)between the observed and predicted HPDs_DY(HPDs_anomaly).The percentage with the same sign for 15 extreme HPDs_anomaly years(PSSE)is 100%.Moreover,in the cross-validation test during 1982–2022,the PSSE for HPDs_anomaly is as high as 100%,along with a low rootmean-square error of 1.14.For independent hindcasts during 2013–2022,the CC between the observed and predicted HPDs_DY(HPDs_anomaly)is 0.93(0.83),together with high Nash-Sutcliffe efficiency(0.82)and agreement index(0.89).Specifically,the predictions are broadly consistent with the observations for 2015,2016,2017,2021,and 2022,reflecting excellent performance of this prediction model of HPDs in NC.展开更多
In this work,a self-healing predictive control method for discrete-time nonlinear systems is presented to ensure the system can be safely operated under abnormal states.First,a robust MPC controller for the normal cas...In this work,a self-healing predictive control method for discrete-time nonlinear systems is presented to ensure the system can be safely operated under abnormal states.First,a robust MPC controller for the normal case is constructed,which can drive the system to the equilibrium point when the closed-loop states are in the predetermined safe set.In this controller,the tubes are built based on the incremental Lyapunov function to tighten nominal constraints.To deal with the infeasible controller when abnormal states occur,a self-healing predictive control method is further proposed to realize self-healing by driving the system towards the safe set.This is achieved by an auxiliary softconstrained recovery mechanism that can solve the constraint violation caused by the abnormal states.By extending the discrete-time robust control barrier function theory,it is proven that the auxiliary problem provides a predictive control barrier bounded function to make the system asymptotically stable towards the safe set.The theoretical properties of robust recursive feasibility and bounded stability are further analyzed.The efficiency of the proposed controller is verified by a numerical simulation of a continuous stirred-tank reactor process.展开更多
Distributed drive electric vehicles(DDEVs)endow the ability to improve vehicle stability performance through direct yaw-moment control(DYC).However,the nonlinear characteristics pose a great challenge to vehicle dynam...Distributed drive electric vehicles(DDEVs)endow the ability to improve vehicle stability performance through direct yaw-moment control(DYC).However,the nonlinear characteristics pose a great challenge to vehicle dynamics control.For this purpose,this paper studies the DYC through the Takagi-Sugeno(T-S)fuzzy-based model predictive control to deal with the nonlinear challenge.First,a T-S fuzzy-based vehicle dynamics model is established to describe the time-varying tire cornering stiffness and vehicle speeds,and thus the uncertain parameters can be represented by the norm-bounded uncertainties.Then,a robust model predictive control(MPC)is developed to guarantee vehicle handling stability.A feasible solution can be obtained through a set of linear matrix inequalities(LMIs).Finally,the tests are conducted by the Carsim/Simulink joint platform to verify the proposed method.The comparative results show that the proposed strategy can effectively guarantee the vehicle’s lateral stability while handling the nonlinear challenge.展开更多
The optical static refractive index,a critical intrinsic property of materials,plays a vital role in advanced optoelectronic applications.Accurate prediction of this index is essential for the efficient design and opt...The optical static refractive index,a critical intrinsic property of materials,plays a vital role in advanced optoelectronic applications.Accurate prediction of this index is essential for the efficient design and optimization of materials with tailored optical properties.Here,we present a robust predictive model that accurately forecasts the optical static refractive indices of complex oxides across diverse crystal structures and compositions.By leveraging chemical bond theory,our model elucidates the influence of intrinsic physical properties,including chemical bonds and d-electron bands,on the refractive index.Through rigorous analysis of 41 complex oxide systems and 5 doped systems,we demonstrate that our predictions align closely with experimental data,showcasing the model’s high accuracy and broad applicability.This work not only accelerates the development of novel materials and spectral design but also provides profound physical insights for optimizing and customizing optical properties.展开更多
In order to compromise the conflicts between control accuracy and system efficiency of conventional electro-hydraulic servo systems,a novel pump-valve coordinated electro-hydraulic servo system was designed and a corr...In order to compromise the conflicts between control accuracy and system efficiency of conventional electro-hydraulic servo systems,a novel pump-valve coordinated electro-hydraulic servo system was designed and a corresponding control strategy was proposed.The system was constituted of a pumpcontrolled part and a valve-controlled part,the pump controlled part is used to adjust the flow rate of oil source and the valve controlled part is used to complete the position tracking control of the hydraulic cylinder.Based on the system characteristics,a load flow grey prediction method was adopted in the pump controlled part to reduce the system overflow losses,and an adaptive robust control method was adopted in the valve controlled part to eliminate the effect of system nonlinearity and parametric uncertainties due to variable hydraulic parameters and system loads on the control precision.The experimental results validated that the adopted control strategy increased the system efficiency obviously with guaranteed high control accuracy.展开更多
Daily influent quantity forecasting plays an important role in sewage treatment plant design and operation. Its uncertain factors are classified into three categories including day types, weather conditions and specia...Daily influent quantity forecasting plays an important role in sewage treatment plant design and operation. Its uncertain factors are classified into three categories including day types, weather conditions and special events, of which the latter two are considered with a BP (Back Propagation) model. On this basis, the daily period feature is taken into account in the presented model. The data from a practical sewage treatment plant utility is employed to show the effectiveness of the method.展开更多
In this paper, the state-feedback Nash game based mixed H2/H∞ design^（[1, 2]）has been extended for output feedback case. The algorithm is applied to control bioreactor system with a Laguerre-Wavelet Network（LWN）^...In this paper, the state-feedback Nash game based mixed H2/H∞ design^（[1, 2]）has been extended for output feedback case. The algorithm is applied to control bioreactor system with a Laguerre-Wavelet Network（LWN）^（[3, 4]）model of the bioreactor.This is achieved by using the LWN model as a deviation model and by successively linearising the deviation model along the state trajectory. For reducing the approximation error and to improve the controller performance, symbolic derivation algorithm, viz.,automatic differentiation is employed. A cautionary note is also given on the fragility of the output feedback mixed H2/H∞ model predictive controller^（[4, 5]）due to its sensitivity to its own parametric changes.展开更多
This paper proposes a robust cooperative control strategy for multiple autonomous vehicles to achieve safe and efficient platoon formation,and it analyzes the effects of vehicle stability boundaries and parameter unce...This paper proposes a robust cooperative control strategy for multiple autonomous vehicles to achieve safe and efficient platoon formation,and it analyzes the effects of vehicle stability boundaries and parameter uncertainties.The cooperative vehicle control framework is composed of the upper planning level and lower tracking control level.In the planning level,the trajectory of each vehicle is generated by using the multi-objective flocking algorithm to form the platoon.The parameters of the flocking algorithm are optimized to prevent the vehicle speed and yaw rate from going beyond their limits.In the lower level,to realize the stable platoon formation,a lumped disturbance observer is designed to gain the stable-state reference,and a distributed robust model predictive controller is proposed to achieve the offset-free trajectory tracking while downsizing the effects of parameter uncertainties.The simulation results show the proposed cooperative control strategy can achieve safe and efficient platoon formation.展开更多
Application of model predictive control(MPC)in horticultural practice requires detailed models.However,even highly sophisticated greenhouse climate models are often known to have unknown dynamics affected by bounded u...Application of model predictive control(MPC)in horticultural practice requires detailed models.However,even highly sophisticated greenhouse climate models are often known to have unknown dynamics affected by bounded uncertainties.To enforce robustness during the controller design stage,this paper proposes a particle swarm optimization(PSO)-based robust MPC strategy for greenhouse temperature systems.The strategy is based on a nonlinear physical temperature affine model.The robust MPC technique requires online solution of a minimax optimal control problem,which optimizes the tradeoff between set point tracking and cost requirements reduction.The minimax optimization problem is reformulated to a nonlinear programming problem with constraints.PSO is used to solve the reformulated problem and priority ranking of constraint fitness is proposed to guarantee that the constraints are satisfied.The results of simulations performed using the proposed control system show that the controller can effectively achieve the set point in the presence of disturbances and that it offers more suitable control variables,higher control precision,and stronger robustness than the conventional MPC.展开更多
Proton Exchange Membrane(PEM)electrolysis stands as a cornerstone technology in the clean energy sector,driving the production of hydrogen and oxygen from water.A critical aspect of ensuring the efficiency and safety ...Proton Exchange Membrane(PEM)electrolysis stands as a cornerstone technology in the clean energy sector,driving the production of hydrogen and oxygen from water.A critical aspect of ensuring the efficiency and safety of this process lies in the precise monitoring and control of temperature at the electrolysis outlet.However,accurately characterizing temperature changes within the PEM electrolysis system can be challenging due to the fluctuation of renewable energies.This study introduces an approach integrating data with fundamental physics principles known as Physics-Informed Neural Networks(PINNs).This method solves differential equations and estimates the unknown parameters governing the temperature dynamics within the PEM electrolysis system.We consider two distinct scenarios:a zero-dimensional model and a one-dimensional model.The results demonstrate the PINN’s proficiency in accurately identifying the parameters and solving for temperature fluctuations within the system with different input conditions.Furthermore,we compare the PINN with the Long Short-Term Memory(LSTM)method to predict the outlet temperature of the electrolysis.The PINN outperformed the LSTM method,highlighting its reliability and precision,achieving a Mean Squared Error(MSE)of 0.1596 compared to 1.2132 for LSTM models.The proposed method shows a high performance in dealing with sensor noises and avoids overfitting problems.This synergy of physics knowledge and data-driven learning opens new pathways towards real-time digital twins,enhanced predictive control,and improved reliability for PEM electrolysis and other complex,data-scarce energy systems.展开更多
The ongoing impact of the novel coronavirus disease 2019(COVID-19)on work and daily life persists as we transition from emergency to normal circumstances.The continuous mutation of viral strains has resulted in a shif...The ongoing impact of the novel coronavirus disease 2019(COVID-19)on work and daily life persists as we transition from emergency to normal circumstances.The continuous mutation of viral strains has resulted in a shift from a single strain to multiple cross-strains,contributing to the spread of the epidemic.Variations in infection rates of the same strain occur because of the implementation of diverse preventive measures at different times.This study investigated the dynamics of the pandemic in the presence of concurrent strains.Building on the classical Susceptible,Exposed,Infected,and Recovered(SEIR)model,a robust piecewise multi-strain cross-epidemic trend prediction model was proposed that employs the Hodges–Lehmann estimator to handle uncertain and contamination-prone epidemic information.A comparative analysis of epidemic spread trend curves across diverse populations using different robust methods revealed the superiority of the Hodges–Lehmann estimator-based model over the traditional method.The accurate prediction results of the model demonstrate its high reliability in tracking the changing trend of the COVID-19 outbreak,thereby supporting its implementation in subsequent epidemic prevention and control measures.展开更多
基金supported by the National Key Research and Development Project of China(2018YFE0122200).
文摘Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.
基金the National Natural Science Founda-tion of China(Nos.72171175,and 72021102)。
文摘This study addresses a new charging station network planning problem for smart connected electric vehicles.We embed a charging station choice model into a charging network planning model that explicitly considers the heterogeneity of the charging behavior in a data-driven manner.To cope with the deficiencies from a small size and sparse behavioral data,we propose a robust charging demand prediction method that can significantly reduce the impact of sample errors and missing data.On the basis of these two building blocks,we form and solve a new optimal charging station location and capacity problem by minimizing the construction and charging costs while considering the charging service level,construction budget,and limit to the number of chargers.We use a case study of planning charging stations in Shanghai to validate our contributions and provide managerial insight in this area.
基金supported by the National Natural Science Foundation of China(62473020).
文摘Dear Editor,This letter presents a novel approach to the data-driven control of unknown nonlinear systems.By leveraging online sparse identification based on the Koopman operator,a high-dimensional linear system model approximating the actual system is obtained online.The upper bound of the discrepancy between the identified model and the actual system is estimated using real-time prediction error,which is then utilized in the design of a tube-based robust model predictive controller.The effectiveness of the proposed approach is validated by numerical simulation.
文摘To solve the attitude trajectory tracking problem for hypersonic vehicles in the presence of system constraints and unknown disturbances,this paper designed a nonlinear robust model predictive control(RMPC)scheme,which can produce near-optimal tracking commands.Unlike the existing designs,the proposed scheme is less conservative and successfully prioritizes the solution optimality.The established RMPC follows a dualloop structure.Specifically,in the outer feedback loop,the reference attitude angle profiles are optimally tracked,while in the inner feedback loop,the control moment commands are produced by optimally tracking the desired angular rate trajectories.Besides,an adaptive disturbance observer(ADO)is designed and embedded in the inner and outer RMPC controllers to alleviate the negative effects caused by unknown external disturbances.The recursive feasibility of the optimization process,together with the input-to-state stability of the proposed RMPC,is theoretically guaranteed by introducing a tightened control constraint and terminal region.The derived property reveals that our proposal can steer the tracking error within a small region of convergence.Finally,the effectiveness of the proposed scheme is demonstrated by performing simulation studies.
基金Project(61673199)supported by the National Natural Science Foundation of ChinaProject(ICT1800400)supported by the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China
文摘For a class of linear discrete-time systems that is subject to randomly occurred networked packet loss in industrial cyber physical systems, a novel robust model predictive control method with active compensation mechanism was proposed. The probability distribution of packet loss is described as the Bernoulli distributed white sequences. By using the Lyapunov stability theory, the existing sufficient conditions of the controller are derived from solving a group of linear matrix inequalities. Moreover, dropout-rate with uncertainty and unknown dropout-rate are also considered, which can greatly reduce the conservativeness of the controller. The designed robust model predictive control method not only efficiently eliminates the negative effects of the networked data loss in industrial cyber physical systems but also ensures the stability of closed-loop system. Two examples were provided to illustrate the superiority and effectiveness of the proposed method.
基金supported by National Natural Science Foundation of China (No. 60934007, No. 61074060)China Postdoctoral Science Foundation (No. 20090460627)+1 种基金Shanghai Postdoctoral Scientific Program (No. 10R21414600)China Postdoctoral Science Foundation Special Support (No. 201003272)
文摘In this paper, a robust model predictive control approach is proposed for a class of uncertain systems with time-varying, linear fractional transformation perturbations. By adopting a sequence of feedback control laws instead of a single one, the control performance can be improved and the region of attraction can be enlarged compared with the existing model predictive control （MPC） approaches. Moreover, a synthesis approach of MPC is developed to achieve high performance with lower on-line computational burden. The effectiveness of the proposed approach is verified by simulation examples.
基金supported by the National Key R&D Program of China(Grant No.2022YFF0801604)the Science and Technology Development Plan in Jilin Province of China(Grant No.20230203135SF)。
文摘Recently,heavy precipitation(HP)events have occurred frequently in North China(NC),causing devastating economic losses and human fatalities.However,the short-term climate prediction of HP is quite limited.Combining year-to-year increment(DY)method and sliding correlations,we developed a robust seasonal prediction model for late-summer HP days(HPDs)in NC during 1982–2022,utilizing three independent predictors—February sea surface temperature(SST)in the Indian Ocean(SST_IO),February snow depth over North Asia(SDE_NA),and May melted snow depth in NC(MSDE_NC).The SST_IO anomalies affect NC's precipitation through the Pacific-Japan pattern.The SDE_NA anomalies are associated with East Asian anomalous anticyclone by southeastern propagation of Rossby wave at Eurasia.The MSDE_NC anomalies are followed by vertical motion and moisture anomalies in situ and thereby cause precipitation anomalies.This prediction model can well simulate the variations of the HPDs,with a correlation coefficient(CC)of 0.81(0.65)between the observed and predicted HPDs_DY(HPDs_anomaly).The percentage with the same sign for 15 extreme HPDs_anomaly years(PSSE)is 100%.Moreover,in the cross-validation test during 1982–2022,the PSSE for HPDs_anomaly is as high as 100%,along with a low rootmean-square error of 1.14.For independent hindcasts during 2013–2022,the CC between the observed and predicted HPDs_DY(HPDs_anomaly)is 0.93(0.83),together with high Nash-Sutcliffe efficiency(0.82)and agreement index(0.89).Specifically,the predictions are broadly consistent with the observations for 2015,2016,2017,2021,and 2022,reflecting excellent performance of this prediction model of HPDs in NC.
基金supported in part the National Key Research and Development Program of China(2021YFC2902703)Open Foundation of State Key Laboratory of Process Automation in Mining&Metallurgy/Beijing Key Laboratory of Process Automation in Mining&Metallurgy(BGRIMM-KZSKL-2022-6)the National Natural Science Foundation of China(62173078,61873049).
文摘In this work,a self-healing predictive control method for discrete-time nonlinear systems is presented to ensure the system can be safely operated under abnormal states.First,a robust MPC controller for the normal case is constructed,which can drive the system to the equilibrium point when the closed-loop states are in the predetermined safe set.In this controller,the tubes are built based on the incremental Lyapunov function to tighten nominal constraints.To deal with the infeasible controller when abnormal states occur,a self-healing predictive control method is further proposed to realize self-healing by driving the system towards the safe set.This is achieved by an auxiliary softconstrained recovery mechanism that can solve the constraint violation caused by the abnormal states.By extending the discrete-time robust control barrier function theory,it is proven that the auxiliary problem provides a predictive control barrier bounded function to make the system asymptotically stable towards the safe set.The theoretical properties of robust recursive feasibility and bounded stability are further analyzed.The efficiency of the proposed controller is verified by a numerical simulation of a continuous stirred-tank reactor process.
基金Supported by National Natural Science Foundation of China(Grant Nos.52402497,52025121 and 52002066)Young Scientists Project and General Project of Applied Basic Research in Yunnan Province(Grant Nos.202501AT070296,202401AU070196)+1 种基金The Key Laboratory of Modern Agricultural Engineering of Ordinary Colleges and Universities of Education Department of Autonomous Region(Grant No.TDNG2023108)Jiangsu Provincial Achievements Transformation Project(Grant No.BA2018023).
文摘Distributed drive electric vehicles(DDEVs)endow the ability to improve vehicle stability performance through direct yaw-moment control(DYC).However,the nonlinear characteristics pose a great challenge to vehicle dynamics control.For this purpose,this paper studies the DYC through the Takagi-Sugeno(T-S)fuzzy-based model predictive control to deal with the nonlinear challenge.First,a T-S fuzzy-based vehicle dynamics model is established to describe the time-varying tire cornering stiffness and vehicle speeds,and thus the uncertain parameters can be represented by the norm-bounded uncertainties.Then,a robust model predictive control(MPC)is developed to guarantee vehicle handling stability.A feasible solution can be obtained through a set of linear matrix inequalities(LMIs).Finally,the tests are conducted by the Carsim/Simulink joint platform to verify the proposed method.The comparative results show that the proposed strategy can effectively guarantee the vehicle’s lateral stability while handling the nonlinear challenge.
基金supported by the National Natural Science Foundation of China(Grant Nos.U23A20565,52301194,52101178)Innovation Program of Shanghai Municipal Education Commission(Grant No.2023ZKZD15)+1 种基金the Shanghai Science and Technology Commission(Grant No.22511100400)the startup funding fromShanghai Jiao Tong University(WH220405009).
文摘The optical static refractive index,a critical intrinsic property of materials,plays a vital role in advanced optoelectronic applications.Accurate prediction of this index is essential for the efficient design and optimization of materials with tailored optical properties.Here,we present a robust predictive model that accurately forecasts the optical static refractive indices of complex oxides across diverse crystal structures and compositions.By leveraging chemical bond theory,our model elucidates the influence of intrinsic physical properties,including chemical bonds and d-electron bands,on the refractive index.Through rigorous analysis of 41 complex oxide systems and 5 doped systems,we demonstrate that our predictions align closely with experimental data,showcasing the model’s high accuracy and broad applicability.This work not only accelerates the development of novel materials and spectral design but also provides profound physical insights for optimizing and customizing optical properties.
基金Supported by Program for New Century Excellent Talents In University(NCET-12-0049)Beijing Natural Science Foundation(4132034)
文摘In order to compromise the conflicts between control accuracy and system efficiency of conventional electro-hydraulic servo systems,a novel pump-valve coordinated electro-hydraulic servo system was designed and a corresponding control strategy was proposed.The system was constituted of a pumpcontrolled part and a valve-controlled part,the pump controlled part is used to adjust the flow rate of oil source and the valve controlled part is used to complete the position tracking control of the hydraulic cylinder.Based on the system characteristics,a load flow grey prediction method was adopted in the pump controlled part to reduce the system overflow losses,and an adaptive robust control method was adopted in the valve controlled part to eliminate the effect of system nonlinearity and parametric uncertainties due to variable hydraulic parameters and system loads on the control precision.The experimental results validated that the adopted control strategy increased the system efficiency obviously with guaranteed high control accuracy.
基金Funded by the National Natural Science Foundation of China (No.59838300)
文摘Daily influent quantity forecasting plays an important role in sewage treatment plant design and operation. Its uncertain factors are classified into three categories including day types, weather conditions and special events, of which the latter two are considered with a BP (Back Propagation) model. On this basis, the daily period feature is taken into account in the presented model. The data from a practical sewage treatment plant utility is employed to show the effectiveness of the method.
文摘In this paper, the state-feedback Nash game based mixed H2/H∞ design^（[1, 2]）has been extended for output feedback case. The algorithm is applied to control bioreactor system with a Laguerre-Wavelet Network（LWN）^（[3, 4]）model of the bioreactor.This is achieved by using the LWN model as a deviation model and by successively linearising the deviation model along the state trajectory. For reducing the approximation error and to improve the controller performance, symbolic derivation algorithm, viz.,automatic differentiation is employed. A cautionary note is also given on the fragility of the output feedback mixed H2/H∞ model predictive controller^（[4, 5]）due to its sensitivity to its own parametric changes.
基金privided by National Natural Science Foundation of China(Grant Nos.51805081,51575103 and U1664258).
文摘This paper proposes a robust cooperative control strategy for multiple autonomous vehicles to achieve safe and efficient platoon formation,and it analyzes the effects of vehicle stability boundaries and parameter uncertainties.The cooperative vehicle control framework is composed of the upper planning level and lower tracking control level.In the planning level,the trajectory of each vehicle is generated by using the multi-objective flocking algorithm to form the platoon.The parameters of the flocking algorithm are optimized to prevent the vehicle speed and yaw rate from going beyond their limits.In the lower level,to realize the stable platoon formation,a lumped disturbance observer is designed to gain the stable-state reference,and a distributed robust model predictive controller is proposed to achieve the offset-free trajectory tracking while downsizing the effects of parameter uncertainties.The simulation results show the proposed cooperative control strategy can achieve safe and efficient platoon formation.
基金supported by the National Natural Science Foundation of China(grant numbers 61174088,60374030).
文摘Application of model predictive control(MPC)in horticultural practice requires detailed models.However,even highly sophisticated greenhouse climate models are often known to have unknown dynamics affected by bounded uncertainties.To enforce robustness during the controller design stage,this paper proposes a particle swarm optimization(PSO)-based robust MPC strategy for greenhouse temperature systems.The strategy is based on a nonlinear physical temperature affine model.The robust MPC technique requires online solution of a minimax optimal control problem,which optimizes the tradeoff between set point tracking and cost requirements reduction.The minimax optimization problem is reformulated to a nonlinear programming problem with constraints.PSO is used to solve the reformulated problem and priority ranking of constraint fitness is proposed to guarantee that the constraints are satisfied.The results of simulations performed using the proposed control system show that the controller can effectively achieve the set point in the presence of disturbances and that it offers more suitable control variables,higher control precision,and stronger robustness than the conventional MPC.
文摘Proton Exchange Membrane(PEM)electrolysis stands as a cornerstone technology in the clean energy sector,driving the production of hydrogen and oxygen from water.A critical aspect of ensuring the efficiency and safety of this process lies in the precise monitoring and control of temperature at the electrolysis outlet.However,accurately characterizing temperature changes within the PEM electrolysis system can be challenging due to the fluctuation of renewable energies.This study introduces an approach integrating data with fundamental physics principles known as Physics-Informed Neural Networks(PINNs).This method solves differential equations and estimates the unknown parameters governing the temperature dynamics within the PEM electrolysis system.We consider two distinct scenarios:a zero-dimensional model and a one-dimensional model.The results demonstrate the PINN’s proficiency in accurately identifying the parameters and solving for temperature fluctuations within the system with different input conditions.Furthermore,we compare the PINN with the Long Short-Term Memory(LSTM)method to predict the outlet temperature of the electrolysis.The PINN outperformed the LSTM method,highlighting its reliability and precision,achieving a Mean Squared Error(MSE)of 0.1596 compared to 1.2132 for LSTM models.The proposed method shows a high performance in dealing with sensor noises and avoids overfitting problems.This synergy of physics knowledge and data-driven learning opens new pathways towards real-time digital twins,enhanced predictive control,and improved reliability for PEM electrolysis and other complex,data-scarce energy systems.
基金This work was supported by National Science Foundation of China with Grant No.721040202022 Science and Technology Think Tank Young Talent Program with Grant No.20220615zz07110051。
文摘The ongoing impact of the novel coronavirus disease 2019(COVID-19)on work and daily life persists as we transition from emergency to normal circumstances.The continuous mutation of viral strains has resulted in a shift from a single strain to multiple cross-strains,contributing to the spread of the epidemic.Variations in infection rates of the same strain occur because of the implementation of diverse preventive measures at different times.This study investigated the dynamics of the pandemic in the presence of concurrent strains.Building on the classical Susceptible,Exposed,Infected,and Recovered(SEIR)model,a robust piecewise multi-strain cross-epidemic trend prediction model was proposed that employs the Hodges–Lehmann estimator to handle uncertain and contamination-prone epidemic information.A comparative analysis of epidemic spread trend curves across diverse populations using different robust methods revealed the superiority of the Hodges–Lehmann estimator-based model over the traditional method.The accurate prediction results of the model demonstrate its high reliability in tracking the changing trend of the COVID-19 outbreak,thereby supporting its implementation in subsequent epidemic prevention and control measures.
