To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitme...To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.展开更多
The thermal induced errors can account for as much as 70% of the dimensional errors on a workpiece. Accurate modeling of errors is an essential part of error compensation. Base on analyzing the existing approaches of ...The thermal induced errors can account for as much as 70% of the dimensional errors on a workpiece. Accurate modeling of errors is an essential part of error compensation. Base on analyzing the existing approaches of the thermal error modeling for machine tools, a new approach of regression orthogonal design is proposed, which combines the statistic theory with machine structures, surrounding condition, engineering judgements, and experience in modeling. A whole computation and analysis procedure is given. Therefore, the model got from this method are more robust and practical than those got from the present method that depends on the modeling data completely. At last more than 100 applications of CNC turning center with only one thermal error model are given. The cutting diameter variation reduces from more than 35 μm to about 12 μm with the orthogonal regression modeling and compensation of thermal error.展开更多
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
In view of the uncertainty in the location selection of logistics distribution center for the fresh agricultural products,the present study established a robust model based on the maximization of principal component s...In view of the uncertainty in the location selection of logistics distribution center for the fresh agricultural products,the present study established a robust model based on the maximization of principal component score taking budget cost parameters as an example.In the process of model solving,the interval form of the uncertain set was used to clarify the constraint conditions,to transform into a certain 0-1 integer linear programming model,so as to solve with the aid of LINGO software.Finally,through studying the location selection of logistics distribution center for fresh agricultural products in the Beijing-Tianjin-Hebei region,it analyzed the application of the robust model and tested the validity of the model.展开更多
Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the hospitals.H...Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the hospitals.Hospital management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital management.It could allow them to take more patients for admission.As a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment.Therefore,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these issues.For this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,etc.have been analyzed to predict the LOS.We selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation criteria.Our results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected models.By Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the hospital.Based on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other diseases.This finding could help predict the future length of hospital stay of new patients,which will help the hospital administration estimate and manage their resources efficiently.展开更多
In this paper, we propose a robust mixture regression model based on the skew scale mixtures of normal distributions (RMR-SSMN) which can accommodate asymmetric, heavy-tailed and contaminated data better. For the vari...In this paper, we propose a robust mixture regression model based on the skew scale mixtures of normal distributions (RMR-SSMN) which can accommodate asymmetric, heavy-tailed and contaminated data better. For the variable selection problem, the penalized likelihood approach with a new combined penalty function which balances the SCAD and l<sub>2</sub> penalty is proposed. The adjusted EM algorithm is presented to get parameter estimates of RMR-SSMN models at a faster convergence rate. As simulations show, our mixture models are more robust than general FMR models and the new combined penalty function outperforms SCAD for variable selection. Finally, the proposed methodology and algorithm are applied to a real data set and achieve reasonable results.展开更多
For the 2-Degree of Freedom(DOF)lower limb exoskeleton,to ensure the system robustness and dynamic performance,a linearextended-state-observer-based(LESO)robust sliding mode control is proposed to not only reduce the ...For the 2-Degree of Freedom(DOF)lower limb exoskeleton,to ensure the system robustness and dynamic performance,a linearextended-state-observer-based(LESO)robust sliding mode control is proposed to not only reduce the influence of parametric uncertainties,unmodeled dynamics,and external disturbance but also estimate the unmeasurable real-time joint angular velocity directly.Then,via Lyapunov technology,the stability of the corresponding LESO and controller is proven.The appropriate and reasonable simulation was carried out to verify the effectiveness of the proposed LESO and exoskeleton controller.展开更多
The influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling engineering.Whereas,there lacks robust methods to predict excavation-induced tunnel displacements.In this study,an au...The influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling engineering.Whereas,there lacks robust methods to predict excavation-induced tunnel displacements.In this study,an auto machine learning(AutoML)-based approach is proposed to precisely solve the issue.Seven input parameters are considered in the database covering two physical aspects,namely soil property,and spatial characteristics of the deep excavation.The 10-fold cross-validation method is employed to overcome the scarcity of data,and promote model’s robustness.Six genetic algorithm(GA)-ML models are established as well for comparison.The results indicated that the proposed AutoML model is a comprehensive model that integrates efficiency and robustness.Importance analysis reveals that the ratio of the average shear strength to the vertical effective stress E_(ur)/σ′_(v),the excavation depth H,and the excavation width B are the most influential variables for the displacements.Finally,the AutoML model is further validated by practical engineering.The prediction results are in a good agreement with monitoring data,signifying that our model can be applied in real projects.展开更多
As traditional Chinese medicines,Fritillaria from different origins are very similar and it is difficult to distinguish them.In this study,the laser-induced breakdown spectroscopy combined with learning vector quantiz...As traditional Chinese medicines,Fritillaria from different origins are very similar and it is difficult to distinguish them.In this study,the laser-induced breakdown spectroscopy combined with learning vector quantization(LIBS-LVQ)was proposed to distinguish the powdered samples of Fritillaria cirrhosa and non-Fritillaria cirrhosa.We also studied the performance of linear discriminant analysis,and support vector machine on the same data set.Among these three classifiers,LVQ had the highest correct classification rate of 99.17%.The experimental results demonstrated that the LIBS-LVQ model could be used to differentiate the powdered samples of Fritillaria cirrhosa and non-Fritillaria cirrhosa.展开更多
Three indexes including forest pest occurrence area,control area and input fund of 31 provinces from 2003 to 2014 were selected from Forestry Statistical Yearbook,to establish dynamic interaction index evaluation syst...Three indexes including forest pest occurrence area,control area and input fund of 31 provinces from 2003 to 2014 were selected from Forestry Statistical Yearbook,to establish dynamic interaction index evaluation system with clustering robust regression model and Stata 13. 0 software. Total forest pest control efficiency in China was determined according to the computing result of entropy method. Suggestions such as improving forest pest control efficiency,increasing service efficiency and input amount of forest pest control input funds were put forward. It will provide empirical basis for target management evaluation of forest pest control work and accountability system.展开更多
Model averaging is a good alternative to model selection,which can deal with the uncertainty from model selection process and make full use of the information from various candidate models.However,most of the existing...Model averaging is a good alternative to model selection,which can deal with the uncertainty from model selection process and make full use of the information from various candidate models.However,most of the existing model averaging criteria do not consider the influence of outliers on the estimation procedures.The purpose of this paper is to develop a robust model averaging approach based on the local outlier factor(LOF)algorithm which can downweight the outliers in the covariates.Asymptotic optimality of the proposed robust model averaging estimator is derived under some regularity conditions.Further,we prove the consistency of the LOF-based weight estimator tending to the theoretically optimal weight vector.Numerical studies including Monte Carlo simulations and a real data example are provided to illustrate our proposed methodology.展开更多
The technological,economic,and environmental benefits of photovoltaic(PV)systems have led to their wide-spread adoption in recent years as a source of electricity generation.However,precisely identifying a PV system’...The technological,economic,and environmental benefits of photovoltaic(PV)systems have led to their wide-spread adoption in recent years as a source of electricity generation.However,precisely identifying a PV system’s maximum power point(MPP)under normal and shaded weather conditions is crucial to conserving the maximum generated power.One of the biggest concerns with a PV system is the existence of partial shading,which produces multiple peaks in the P–V characteristic curve.In these circumstances,classical maximum power point tracking(MPPT)approaches are prone to getting stuck on local peaks and failing to follow the global maximum power point(GMPP).To overcome such obstacles,a new Lyapunov-based Robust Model Reference Adaptive Controller(LRMRAC)is designed and implemented to reach GMPP rapidly and ripple-free.The proposed controller also achieves MPP accurately under slow,abrupt and rapid changes in radiation,temperature and load profile.Simulation and OPAL-RT real-time simulators in various scenarios are performed to verify the superiority of the proposed approach over the other state-of-the-art methods,i.e.,ANFIS,INC,VSPO,and P&O.MPP and GMPP are accomplished in less than 3.8 ms and 10 ms,respectively.Based on the results presented,the LRMRAC controller appears to be a promising technique for MPPT in a PV system.展开更多
Disturbance-Free Payload(DFP)spacecraft can meet the requirements of ultra-high attitude pointing accuracy and stability for future space missions.However,as the main control actuators of DFP spacecraft,Linear Non-Con...Disturbance-Free Payload(DFP)spacecraft can meet the requirements of ultra-high attitude pointing accuracy and stability for future space missions.However,as the main control actuators of DFP spacecraft,Linear Non-Contact Lorentz Actuators(LNCLAs)have control output problems with six-degree-of-freedom coupling and nonlinear effects,which will affect the attitude control performance of DFP spacecraft.To solve this problem,a novel concept for Non-Contact Annular Electromagnetic Stabilized Satellite Platform(NCAESSP)is proposed in this study.The concept is centered on replacing the LNCLAs with a non-contact annular electromagnetic actuator to solve the two problems mentioned above.Furthermore,for the different control requirements of the payload module and the support module of the NCAESSP,a high-precision attitude controller based on the robust model matching method and a dual quaternion-based adaptive sliding mode controller are proposed.Additionally,the simulation results verify the feasibility and effectiveness of the proposed approach.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
Block principle component analysis(BPCA) is a recently developed technique in computer vision and pattern classification. In this paper, we propose a robust and sparse BPCA with Lp-norm, referred to as BPCALp-S, which...Block principle component analysis(BPCA) is a recently developed technique in computer vision and pattern classification. In this paper, we propose a robust and sparse BPCA with Lp-norm, referred to as BPCALp-S, which inherits the robustness of BPCA-L1 due to the employment of adjustable Lp-norm. In order to perform a sparse modelling, the elastic net is integrated into the objective function. An iterative algorithm which extracts feature vectors one by one greedily is elaborately designed. The monotonicity of the proposed iterative procedure is theoretically guaranteed. Experiments of image classification and reconstruction on several benchmark sets show the effectiveness of the proposed approach.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
This paper proposes a sliding mode controller based on robust model reference adaptive proportional-integral(RMRA-PI)control for a stand-alone voltage source inverter(SA-VSI).The proposed controller has two control lo...This paper proposes a sliding mode controller based on robust model reference adaptive proportional-integral(RMRA-PI)control for a stand-alone voltage source inverter(SA-VSI).The proposed controller has two control loops where the coefficients of PI controller are regulated by the adaptive sliding law.This method is used to regulate the output voltage of the inverter under different load conditions and uncertainty,and adapts the output to the reference model to reduce the total harmonic distortion(THD).In this paper,the stability of the proposed controller is proven by using Lyapunov's theory and Barbalet’s lemma.The proposed controller performs well in voltage regulation such as low THD under sudden load change and uncertainty.Also,the results of the proposed controller are compared with PI controller to show the effectiveness of the presented control system.展开更多
This paper addresses the planning problem of residential micro combined heat and power (micro-CHP) systems (including micro-generation units, auxiliary boilers, and thermal storage tanks) considering the associated te...This paper addresses the planning problem of residential micro combined heat and power (micro-CHP) systems (including micro-generation units, auxiliary boilers, and thermal storage tanks) considering the associated technical and economic factors. Since the accurate values of the thermal and electrical loads of this system cannot be exactly predicted for the planning horizon, the thermal and electrical load uncertainties are modeled using a two-stage adaptive robust optimization method based on a polyhedral uncertainty set. A solution method, which is composed of column-and-constraint generation (C&CG) algorithm and block coordinate descent (BCD) method, is proposed to efficiently solve this adaptive robust optimization model. Numerical results from a practical case study show the effective performance of the proposed adaptive robust model for residential micro-CHP planning and its solution method.展开更多
The relationship between air pollution and cerebrovascular disease has become a popular topic,yet research findings are highly heterogeneous.This study aims to investigate this association based on detailed individual...The relationship between air pollution and cerebrovascular disease has become a popular topic,yet research findings are highly heterogeneous.This study aims to investigate this association based on detailed individual health data and a precise evaluation of their exposure levels.The integrated models of generalized additive model,land use regression model and back propagation neural network were used to evaluate the exposure concentrations.And doubly robust additive model was conducted to explore the association between cerebrovascular disease and air pollution after adjusted for demographic characteristics,physical examination,disease information,geographic and socioeconomic status.A total of 25097 subjects were included in the Beijing Health Management Cohort from 2013 to 2018.With a 1µg/m^(3)increase in the concentrations of PM_(2.5),SO_(2)and NO_(2),the incidence risk of cerebrovascular disease increased by 1.02(95%CI:1.008–1.034),1.06(95%CI:1.034–1.095)and 1.02(95%CI:1.010–1.029)respectively.Whereas CO exposure could decrease the risk,with an odds ratio of 0.38(95%CI:0.212–0.626).In the subgroup analysis,individuals under the age of 50 with normal BMI were at higher risk caused by PM2.5,and So2 was considered more hazardous to women.Meanwhile,the protective effect of CO on women and those with normal BMI was stronger.Successful reduction of long-term exposure to PM2.5,SO_(2)and NO_(2)would lead to substantial benefits for decrease the risk of cerebrovascular disease especially for the health of the susceptible individuals.展开更多
基金supported by the Special Research Project on Power Planning of the Guangdong Power Grid Co.,Ltd.
文摘To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.
文摘The thermal induced errors can account for as much as 70% of the dimensional errors on a workpiece. Accurate modeling of errors is an essential part of error compensation. Base on analyzing the existing approaches of the thermal error modeling for machine tools, a new approach of regression orthogonal design is proposed, which combines the statistic theory with machine structures, surrounding condition, engineering judgements, and experience in modeling. A whole computation and analysis procedure is given. Therefore, the model got from this method are more robust and practical than those got from the present method that depends on the modeling data completely. At last more than 100 applications of CNC turning center with only one thermal error model are given. The cutting diameter variation reduces from more than 35 μm to about 12 μm with the orthogonal regression modeling and compensation of thermal error.
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Supported by Student Innovation and Entrepreneurship Training Program Project of Hebei Agricultural University(2020102).
文摘In view of the uncertainty in the location selection of logistics distribution center for the fresh agricultural products,the present study established a robust model based on the maximization of principal component score taking budget cost parameters as an example.In the process of model solving,the interval form of the uncertain set was used to clarify the constraint conditions,to transform into a certain 0-1 integer linear programming model,so as to solve with the aid of LINGO software.Finally,through studying the location selection of logistics distribution center for fresh agricultural products in the Beijing-Tianjin-Hebei region,it analyzed the application of the robust model and tested the validity of the model.
文摘Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the hospitals.Hospital management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital management.It could allow them to take more patients for admission.As a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment.Therefore,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these issues.For this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,etc.have been analyzed to predict the LOS.We selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation criteria.Our results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected models.By Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the hospital.Based on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other diseases.This finding could help predict the future length of hospital stay of new patients,which will help the hospital administration estimate and manage their resources efficiently.
文摘In this paper, we propose a robust mixture regression model based on the skew scale mixtures of normal distributions (RMR-SSMN) which can accommodate asymmetric, heavy-tailed and contaminated data better. For the variable selection problem, the penalized likelihood approach with a new combined penalty function which balances the SCAD and l<sub>2</sub> penalty is proposed. The adjusted EM algorithm is presented to get parameter estimates of RMR-SSMN models at a faster convergence rate. As simulations show, our mixture models are more robust than general FMR models and the new combined penalty function outperforms SCAD for variable selection. Finally, the proposed methodology and algorithm are applied to a real data set and achieve reasonable results.
基金This work was supported by National Natural Science Foundation of China(No.51775089 and 11872147)Sichuan Science and Technology Program(No.2018JY0565 and 2020YFG0137).
文摘For the 2-Degree of Freedom(DOF)lower limb exoskeleton,to ensure the system robustness and dynamic performance,a linearextended-state-observer-based(LESO)robust sliding mode control is proposed to not only reduce the influence of parametric uncertainties,unmodeled dynamics,and external disturbance but also estimate the unmeasurable real-time joint angular velocity directly.Then,via Lyapunov technology,the stability of the corresponding LESO and controller is proven.The appropriate and reasonable simulation was carried out to verify the effectiveness of the proposed LESO and exoskeleton controller.
基金supported by the National Natural Science Foundation of China(Grant Nos.51978517,52090082,and 52108381)Innovation Program of Shanghai Municipal Education Commission(Grant No.2019-01-07-00-07-456 E00051)Shanghai Science and Technology Committee Program(Grant Nos.21DZ1200601 and 20DZ1201404).
文摘The influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling engineering.Whereas,there lacks robust methods to predict excavation-induced tunnel displacements.In this study,an auto machine learning(AutoML)-based approach is proposed to precisely solve the issue.Seven input parameters are considered in the database covering two physical aspects,namely soil property,and spatial characteristics of the deep excavation.The 10-fold cross-validation method is employed to overcome the scarcity of data,and promote model’s robustness.Six genetic algorithm(GA)-ML models are established as well for comparison.The results indicated that the proposed AutoML model is a comprehensive model that integrates efficiency and robustness.Importance analysis reveals that the ratio of the average shear strength to the vertical effective stress E_(ur)/σ′_(v),the excavation depth H,and the excavation width B are the most influential variables for the displacements.Finally,the AutoML model is further validated by practical engineering.The prediction results are in a good agreement with monitoring data,signifying that our model can be applied in real projects.
基金supported by National Natural Science Foundation of China(No.62075011)Graduate Technological Innovation Project of Beijing Institute of Technology(No.2019CX20026)。
文摘As traditional Chinese medicines,Fritillaria from different origins are very similar and it is difficult to distinguish them.In this study,the laser-induced breakdown spectroscopy combined with learning vector quantization(LIBS-LVQ)was proposed to distinguish the powdered samples of Fritillaria cirrhosa and non-Fritillaria cirrhosa.We also studied the performance of linear discriminant analysis,and support vector machine on the same data set.Among these three classifiers,LVQ had the highest correct classification rate of 99.17%.The experimental results demonstrated that the LIBS-LVQ model could be used to differentiate the powdered samples of Fritillaria cirrhosa and non-Fritillaria cirrhosa.
基金Supported by Analysis of Forest Pest Cost Responsibility Investigation System(2017-R04)Protection and Development:Coordination Mechanism Research from the Perspective of Community(71373024)
文摘Three indexes including forest pest occurrence area,control area and input fund of 31 provinces from 2003 to 2014 were selected from Forestry Statistical Yearbook,to establish dynamic interaction index evaluation system with clustering robust regression model and Stata 13. 0 software. Total forest pest control efficiency in China was determined according to the computing result of entropy method. Suggestions such as improving forest pest control efficiency,increasing service efficiency and input amount of forest pest control input funds were put forward. It will provide empirical basis for target management evaluation of forest pest control work and accountability system.
基金supported by the National Natural Science Foundation of China (Grant Nos.11971323,12031016).
文摘Model averaging is a good alternative to model selection,which can deal with the uncertainty from model selection process and make full use of the information from various candidate models.However,most of the existing model averaging criteria do not consider the influence of outliers on the estimation procedures.The purpose of this paper is to develop a robust model averaging approach based on the local outlier factor(LOF)algorithm which can downweight the outliers in the covariates.Asymptotic optimality of the proposed robust model averaging estimator is derived under some regularity conditions.Further,we prove the consistency of the LOF-based weight estimator tending to the theoretically optimal weight vector.Numerical studies including Monte Carlo simulations and a real data example are provided to illustrate our proposed methodology.
文摘The technological,economic,and environmental benefits of photovoltaic(PV)systems have led to their wide-spread adoption in recent years as a source of electricity generation.However,precisely identifying a PV system’s maximum power point(MPP)under normal and shaded weather conditions is crucial to conserving the maximum generated power.One of the biggest concerns with a PV system is the existence of partial shading,which produces multiple peaks in the P–V characteristic curve.In these circumstances,classical maximum power point tracking(MPPT)approaches are prone to getting stuck on local peaks and failing to follow the global maximum power point(GMPP).To overcome such obstacles,a new Lyapunov-based Robust Model Reference Adaptive Controller(LRMRAC)is designed and implemented to reach GMPP rapidly and ripple-free.The proposed controller also achieves MPP accurately under slow,abrupt and rapid changes in radiation,temperature and load profile.Simulation and OPAL-RT real-time simulators in various scenarios are performed to verify the superiority of the proposed approach over the other state-of-the-art methods,i.e.,ANFIS,INC,VSPO,and P&O.MPP and GMPP are accomplished in less than 3.8 ms and 10 ms,respectively.Based on the results presented,the LRMRAC controller appears to be a promising technique for MPPT in a PV system.
基金co-supported by the National Natural Science Foundation of China(No.12172168)。
文摘Disturbance-Free Payload(DFP)spacecraft can meet the requirements of ultra-high attitude pointing accuracy and stability for future space missions.However,as the main control actuators of DFP spacecraft,Linear Non-Contact Lorentz Actuators(LNCLAs)have control output problems with six-degree-of-freedom coupling and nonlinear effects,which will affect the attitude control performance of DFP spacecraft.To solve this problem,a novel concept for Non-Contact Annular Electromagnetic Stabilized Satellite Platform(NCAESSP)is proposed in this study.The concept is centered on replacing the LNCLAs with a non-contact annular electromagnetic actuator to solve the two problems mentioned above.Furthermore,for the different control requirements of the payload module and the support module of the NCAESSP,a high-precision attitude controller based on the robust model matching method and a dual quaternion-based adaptive sliding mode controller are proposed.Additionally,the simulation results verify the feasibility and effectiveness of the proposed approach.
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the National Natural Science Foundation of China(No.61572033)the Natural Science Foundation of Education Department of Anhui Province of China(No.KJ2015ZD08)the Higher Education Promotion Plan of Anhui Province of China(No.TSKJ2015B14)
文摘Block principle component analysis(BPCA) is a recently developed technique in computer vision and pattern classification. In this paper, we propose a robust and sparse BPCA with Lp-norm, referred to as BPCALp-S, which inherits the robustness of BPCA-L1 due to the employment of adjustable Lp-norm. In order to perform a sparse modelling, the elastic net is integrated into the objective function. An iterative algorithm which extracts feature vectors one by one greedily is elaborately designed. The monotonicity of the proposed iterative procedure is theoretically guaranteed. Experiments of image classification and reconstruction on several benchmark sets show the effectiveness of the proposed approach.
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This paper proposes a sliding mode controller based on robust model reference adaptive proportional-integral(RMRA-PI)control for a stand-alone voltage source inverter(SA-VSI).The proposed controller has two control loops where the coefficients of PI controller are regulated by the adaptive sliding law.This method is used to regulate the output voltage of the inverter under different load conditions and uncertainty,and adapts the output to the reference model to reduce the total harmonic distortion(THD).In this paper,the stability of the proposed controller is proven by using Lyapunov's theory and Barbalet’s lemma.The proposed controller performs well in voltage regulation such as low THD under sudden load change and uncertainty.Also,the results of the proposed controller are compared with PI controller to show the effectiveness of the presented control system.
文摘This paper addresses the planning problem of residential micro combined heat and power (micro-CHP) systems (including micro-generation units, auxiliary boilers, and thermal storage tanks) considering the associated technical and economic factors. Since the accurate values of the thermal and electrical loads of this system cannot be exactly predicted for the planning horizon, the thermal and electrical load uncertainties are modeled using a two-stage adaptive robust optimization method based on a polyhedral uncertainty set. A solution method, which is composed of column-and-constraint generation (C&CG) algorithm and block coordinate descent (BCD) method, is proposed to efficiently solve this adaptive robust optimization model. Numerical results from a practical case study show the effective performance of the proposed adaptive robust model for residential micro-CHP planning and its solution method.
基金the National Natural Science Foundation of China(No.81773512).
文摘The relationship between air pollution and cerebrovascular disease has become a popular topic,yet research findings are highly heterogeneous.This study aims to investigate this association based on detailed individual health data and a precise evaluation of their exposure levels.The integrated models of generalized additive model,land use regression model and back propagation neural network were used to evaluate the exposure concentrations.And doubly robust additive model was conducted to explore the association between cerebrovascular disease and air pollution after adjusted for demographic characteristics,physical examination,disease information,geographic and socioeconomic status.A total of 25097 subjects were included in the Beijing Health Management Cohort from 2013 to 2018.With a 1µg/m^(3)increase in the concentrations of PM_(2.5),SO_(2)and NO_(2),the incidence risk of cerebrovascular disease increased by 1.02(95%CI:1.008–1.034),1.06(95%CI:1.034–1.095)and 1.02(95%CI:1.010–1.029)respectively.Whereas CO exposure could decrease the risk,with an odds ratio of 0.38(95%CI:0.212–0.626).In the subgroup analysis,individuals under the age of 50 with normal BMI were at higher risk caused by PM2.5,and So2 was considered more hazardous to women.Meanwhile,the protective effect of CO on women and those with normal BMI was stronger.Successful reduction of long-term exposure to PM2.5,SO_(2)and NO_(2)would lead to substantial benefits for decrease the risk of cerebrovascular disease especially for the health of the susceptible individuals.
