With the increasing number of geosynchronous orbit satellites with expiring lifetime,spacecraft refueling is crucial in enhancing the economic benefits of on-orbit services.The existing studies tend to be based on pre...With the increasing number of geosynchronous orbit satellites with expiring lifetime,spacecraft refueling is crucial in enhancing the economic benefits of on-orbit services.The existing studies tend to be based on predetermined refueling duration;however,the precise mission scheduling solution will be difficult to apply due to uncertain refueling duration caused by orbital transfer deviations and stochastic actuator faults during actual on-orbit service.Therefore,this paper proposes a robust mission scheduling strategy for geosynchronous orbit spacecraft on-orbit refueling missions with uncertain refueling duration.Firstly,a robust mission scheduling model is constructed by introducing the budget uncertainty set to describe the uncertain refueling duration.Secondly,a hybrid harris hawks optimization algorithm is designed to explore the optimal mission allocation and refueling sequences,which combines cubic chaotic mapping to initialize the population,and the crossover in the genetic algorithm is introduced to enhance global convergence.Finally,the typical simulation examples are constructed with real-mission scenarios in three aspects to analyze:performance comparisons with various algorithms;robustness analyses via comparisons of different on-orbit refueling durations;investigations into the impacts of different initial population strategies on algorithm performance,demonstrating the proposed mission scheduling framework's robustness and effectiveness by comparing it with the exact mission scheduling.展开更多
Although industrial processes often perform perfectly under design conditions, they may deviate from the optimal operating point owing to parameters drift, environmental disturbances, etc. Thus, it is necessary to dev...Although industrial processes often perform perfectly under design conditions, they may deviate from the optimal operating point owing to parameters drift, environmental disturbances, etc. Thus, it is necessary to develop efficacious strategies or procedure to assess the process performance online. In this paper, we explore the issue of operating optimality assessment for complex industrial processes based on performance-similarity considering nonlinearities and outliers simultaneously, and a general enforced online performance assessment framework is proposed. In the offline part, a new and modified total robust kernel projection to latent structures algorithm,T-KPRM, is proposed and used to evaluate the complex nonlinear industrial process, which can effectively extract the optimal-index-related process variation information from process data and establish assessment models for each performance grades overcoming the effects of outlier. In the online part, the online assessment results can be obtained by calculating the similarity between the online data from a sliding window and each of the performance grades. Furthermore, in order to improve the accuracy of online assessment, we propose an online assessment strategy taking account of the effects of noise and process uncertainties. The Euclidean distance between the sliding data window and the optimal evaluation level is employed to measure the contribution rates of variables, which indicate the possible reason for the non-optimal operating performance. The proposed framework is tested on a real industrial case: dense medium coal preparation process, and the results shows the efficiency of the proposed method comparing to the existing method.展开更多
The Kelly strategy is a common approach in portfolio optimization problems that aims to maximize the expected portfolio growth rate in the long term.Its computation requires complete knowledge of the asset return dist...The Kelly strategy is a common approach in portfolio optimization problems that aims to maximize the expected portfolio growth rate in the long term.Its computation requires complete knowledge of the asset return distribution,which is obviously not observable,but can be inferred from sample data.Motivated by recent developments in data-driven optimization methods,we propose a new class of coherent Wasserstein data-driven Kelly portfolio optimization models.In particular,we establish a class of ambiguity sets based on coherent Wasserstein metrics,and these new metrics can strike a good balance between robustness and data-drivenness,thus providing richer choices for ambiguity set design.The Kelly portfolio optimization model,which is data-driven and based on coherent Wasserstein balls,can be solved efficiently as a finite-dimensional convex program.This model also provides a robust data-driven solution.In addition,we numerically investigate the proposed model and find that it outperforms the type-1 Wasserstein-Kelly portfolio,especially the classical Kelly portfolio.Moreover,it indicates that we can obtain a portfolio with higher final value and stability,especially in controlling volatility and maximum drawdown.展开更多
Dear Editor,With the advances in computing and communication technologies,the cyber-physical system(CPS),has been used in lots of industrial fields,such as the urban water cycle,internet of things,and human-cyber syst...Dear Editor,With the advances in computing and communication technologies,the cyber-physical system(CPS),has been used in lots of industrial fields,such as the urban water cycle,internet of things,and human-cyber systems[1],[2],which has to face up to malicious cyber-attacks towards cyber communication of control commands.Specifically,jamming attack is regarded as one of the most common attacks of decreasing network performance.Game theory is widely regarded as a method of accurately describing the interaction between jamming attacker and legitimate user[3].In the cyber layer,the signal game model has been utilized to describe the transmission between the attacker and defender[4].However,most previous game theoretical researches are not feasible to meet the demands of industrial CPSs mainly due to the shared communication network nature.Specifically,it leads to incomplete information for players of game owing to various network-induced phenomena and employed communication protocols.In the physical layer,the secure control[5]and estimation[6]under attack detection have been studied for CPSs.However,these methods not only rely heavily on signals injection detection,but also have no access to smart attackers who launch covert attacks so that data receivers cannot observe the attack behaviour[7].Accordingly,the motivation arising here is to tackle the nested game problem for CPSs subject to jamming attack.展开更多
Trajectory planning under uncertain dynamics is critical for safety-critical systems like Unmanned Aerial Vehicles(UAVs),where uncertainties in aerodynamic force and control surface failure can lead to mission failure...Trajectory planning under uncertain dynamics is critical for safety-critical systems like Unmanned Aerial Vehicles(UAVs),where uncertainties in aerodynamic force and control surface failure can lead to mission failure.This paper proposes a Multi-stage Robust Optimization(MRO)framework to address nonlinear trajectory planning with bounded but unknown parameters.By integrating first-order sensitivity analysis and sequential optimization,the proposed method ensures robustness against worst-case parameter deviations while maintaining high terminal accuracy.Unlike existing approaches,this paper explicitly quantifies uncertainty propagation through sensitivity bounds and divides long-term planning into sub-stages to reduce cumulative errors.Simulations on a UAV model with uncertainties in aerodynamic coefficients,wind fields and coefficients of control inputs demonstrate that MRO achieves high terminal state accuracy and strong robustness.展开更多
Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainabili...Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainability through coordinated electricity,thermal,natural gas,and hydrogen utilization.This study proposes a two-stage distributionally robust optimization(DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES.The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response(DR)program to adjustflexible multi-energy loads,thereby prioritizing RES consumption.Uncertainties from RES generation and load demand are addressed through an ambiguity set,enabling robust decision-making.The column-and-constraint generation(C&CG)algorithm efficiently solves the two-stage DRO model.Case studies demonstrate that the proposed method reduces operational costs by 3.56%,increases photovoltaic consumption rates by 5.44%,and significantly lowers carbon emissions compared to conventional approaches.Furthermore,the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods,highlighting its potential to advance cost-effective,low-carbon energy systems while ensuring grid stability under uncertainty.展开更多
Most enterprises rely on railway transportation to deliver their products to customers,particularly in the salt lake chemical industry.Notably,allocating products to freight spaces and their assembly on transport vehi...Most enterprises rely on railway transportation to deliver their products to customers,particularly in the salt lake chemical industry.Notably,allocating products to freight spaces and their assembly on transport vehicles are critical pre-transportation processes.However,due to demand fluctuations from changing product orders and unforeseen railway scheduling delays,manually adjusted allocation and loading may lead to excessive loading and unloading distances and times,ultimately increasing transportation costs for enterprises.To address these issues,this paper proposes a data-driven two-stage robust optimization(TSRO)framework embedding with the gated stacked temporal autoencoder clustering based on the attention mechanism(GSTAC-AM),which aims to overcome demand uncertainty and enhance the efficiency of freight allocation and loading.Specifically,GSTAC-AM is developed to help predict the deviation level of demand uncertainty and mitigate the impact of potential outliers.Then,a robust counterpart model is formulated to ensure computational tractability.In addition,a multi-stage hybrid heuristic algorithm is designed to handle the large scale and complexity inherent in the freight space allocation and loading processes.Finally,the effectiveness and applicability of the proposed framework are validated through a real case study conducted in a large salt lake chemical enterprise.展开更多
In this study,we construct a bi-level optimization model based on the Stackelberg game and propose a robust optimization algorithm for solving the bi-level model,assuming an actual situation with several participants ...In this study,we construct a bi-level optimization model based on the Stackelberg game and propose a robust optimization algorithm for solving the bi-level model,assuming an actual situation with several participants in energy trading.Firstly,the energy trading process is analyzed between each subject based on the establishment of the operation framework of multi-agent participation in energy trading.Secondly,the optimal operation model of each energy trading agent is established to develop a bi-level game model including each energy participant.Finally,a combination algorithm of improved robust optimization over time(ROOT)and CPLEX is proposed to solve the established game model.The experimental results indicate that under different fitness thresholds,the robust optimization results of the proposed algorithm are increased by 56.91%and 68.54%,respectively.The established bi-level game model effectively balances the benefits of different energy trading entities.The proposed algorithm proposed can increase the income of each participant in the game by an average of 8.59%.展开更多
Trapped ion hardware has made significant progress recently and is now one of the leading platforms for quantum computing.To construct two-qubit gates in trapped ions,experimentalmanipulation approaches for ion chains...Trapped ion hardware has made significant progress recently and is now one of the leading platforms for quantum computing.To construct two-qubit gates in trapped ions,experimentalmanipulation approaches for ion chains are becoming increasingly prevalent.Given the restricted control technology,how implementing high-fidelity quantum gate operations is crucial.Many works in current pulse design optimization focus on ion–phonon and effective ion–ion couplings while ignoring the first-order derivative terms expansion impacts of these two terms brought on by experiment defects.This paper proposes a novel robust quantum control optimization method in trapped ions.By introducing the first-order derivative terms caused by the error into the optimization cost function,we generate an extremely robust Mølmer–Sørensen gate with infidelity below 10^(−3) under a drift noise range of±10 kHz,the relative robustness achieves a tolerance of±5%,compared to the 200-kHz frequency spacing between phonon modes,and for time noise drift,the tolerance reached to 2%.Our work reveals the vital role of the first-order derivative terms of coupling in trapped ion pulse control optimization,especially the first-order derivative terms of ion–ion coupling.It provides a robust optimization scheme for realizing more efficient entangled states in trapped ion platforms.展开更多
A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncer...A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.展开更多
We present a robust quantum optimal control framework for implementing fast entangling gates on ion-trap quantum processors.The framework leverages tailored laser pulses to drive the multiple vibrational sidebands of ...We present a robust quantum optimal control framework for implementing fast entangling gates on ion-trap quantum processors.The framework leverages tailored laser pulses to drive the multiple vibrational sidebands of the ions to create phonon-mediated entangling gates and,unlike the state of the art,requires neither weakcoupling Lamb-Dicke approximation nor perturbation treatment.With the application of gradient-based optimal control,it enables finding amplitude-and phase-modulated laser control protocols that work without the Lamb-Dicke approximation,promising gate speeds on the order of microseconds comparable to the characteristic trap frequencies.Also,robustness requirements on the temperature of the ions and initial optical phase can be conveniently included to pursue high-quality fast gates against experimental imperfections.Our approach represents a step in speeding up quantum gates to achieve larger quantum circuits for quantum computation and simulation,and thus can find applications in near-future experiments.展开更多
To enhance the low-carbon economic efficiency and increase the utilization of renewable energy within integrated energy systems(IES),this paper proposes a low-carbon dispatch model integrating power-to-gas(P2G),carbon...To enhance the low-carbon economic efficiency and increase the utilization of renewable energy within integrated energy systems(IES),this paper proposes a low-carbon dispatch model integrating power-to-gas(P2G),carbon capture and storage(CCS),hydrogen fuel cell(HFC),and combined heat and power(CHP).The P2G process is refined into a two-stage structure,and HFC is introduced to enhance hydrogen utilization.Together with CCS and CHP,these devices form a multi-energy conversion system coupling electricity,heat,cooling,and gas.A laddertype carbon trading approach is adopted to flexibly manage carbon output by leveraging marginal cost adjustments.To evaluate the resilience of the proposed low-carbon scheduling strategy involving multiple energy units under the variability of renewable energy,a two-level robust optimization framework is developed.This model captures the most adverse scenarios of wind and solar generation.The dispatch strategy is validated against these extreme conditions to demonstrate its flexibility and effectiveness.The problem is solved using the GUROBI optimization tool.Results from simulations indicate that themodel increases renewable energy integration by 39.1%,and achieves reductions of 15.96%in carbon emissions and 16.29%in operational expenditures.The results demonstrate that the strategy ensures both economic efficiency and environmental performance under uncertain conditions.Compared with existing studies that separately model two-stage P2G or CCS devices,this paper integrates HFC,CHP,and CCS into a unified dispatchable system,enabling refined hydrogen utilization and flexible carbon circulation.Furthermore,the introduction of a laddertype carbon pricing mechanism,combined with multi-energy storage participation in implicit demand response,creates a dynamic and cost-sensitive dispatch framework.These modeling strategies go beyond conventional linear IES formulations and provide more realistic system representations.The proposed approach not only deepens the coupling among electric,thermal,and gas systems,but also offers a feasible pathway for high-penetration renewable integration in low-carbon energy systems.展开更多
Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always ...Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.展开更多
Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous congestio...Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous congestion and further reduce the risk of cross-infection,a novel two-stage distributionally robust optimization(DRO)model is explicitly constructed,in which the probability distribution of stochastic scenarios is only partially known in advance.In the proposed model,the mean-conditional value-at-risk(CVaR)criterion is employed to obtain a tradeoff between the expected number of waiting passen-gers and the risk of congestion on an urban rail transit line.The relationship between the proposed DRO model and the traditional two-stage stochastic programming(SP)model is also depicted.Furthermore,to overcome the obstacle of model solvability resulting from imprecise probability distributions,a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form.A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming(MILP)solver is developed to improve the computational efficiency of large-scale instances.Finally,a series of numerical examples with real-world operation data are executed to validate the pro-posed approaches.展开更多
Blade fouling has been proved to be a great threat to compressor performance in operating stage.The current researches on fouling-induced performance degradations of centrifugal compressors are based mainly on simplif...Blade fouling has been proved to be a great threat to compressor performance in operating stage.The current researches on fouling-induced performance degradations of centrifugal compressors are based mainly on simplified roughness models without taking into account the realistic factors such as spatial non-uniformity and randomness of the fouling-induced surface roughness.Moreover,little attention has been paid to the robust design optimization of centrifugal compressor impellers with considerations of blade fouling.In this paper,a multi-objective robust design optimization method is developed for centrifugal impellers under surface roughness uncertainties due to blade fouling.A three-dimensional surface roughness map is proposed to describe the nonuniformity and randomness of realistic fouling accumulations on blades.To lower computational cost in robust design optimization,the support vector regression(SVR)metamodel is combined with the Monte Carlo simulation(MCS)method to conduct the uncertainty analysis of fouled impeller performance.The analyzed results show that the critical fouled region associated with impeller performance degradations lies at the leading edge of blade tip.The SVR metamodel has been proved to be an efficient and accurate means in the detection of impeller performance variations caused by roughness uncertainties.After design optimization,the robust optimal design is found to be more efficient and less sensitive to fouling uncertainties while maintaining good impeller performance in the clean condition.This research proposes a systematic design optimization method for centrifugal compressors with considerations of blade fouling,providing a practical guidance to the design of advanced centrifugal compressors.展开更多
This paper examines robust optimization design and analysis of a conformal expansion nozzle of flying wing Unmanned Aerial Vehicle （UAV） with the inverse-design idea. In view of flow features and stealth constraints...This paper examines robust optimization design and analysis of a conformal expansion nozzle of flying wing Unmanned Aerial Vehicle （UAV） with the inverse-design idea. In view of flow features and stealth constraints, the inverse-design idea is described and the uncertainty-based robust design model is presented. A robust design system employs this model to combine deterministic optimization and robust optimization and is applied into design of a conformal expansion nozzle. The results indicate that design optimization can conform to the anticipation of the inversedesign idea and significantly improve the aerodynamic performance that meet the requirement of 6σ. The present method is a feasible nozzle design strategy that integrates robust optimization and inverse-design.展开更多
This paper reviews existing approaches to the airport gate assignment problem （AGAP） and presents an optimization model for the problem considering operational safety constraints. The main objective is to minimize t...This paper reviews existing approaches to the airport gate assignment problem （AGAP） and presents an optimization model for the problem considering operational safety constraints. The main objective is to minimize the dispersion of gate idle time periods （to get robust optimization） while ensuring appropriate matching between the size of each aircraft and its assigned gate type and avoiding the potential hazard caused by gate apron operational conflict. Genetic algorithm is adopted to solve the problem, An illustrative example is given to show the effectiveness and efficiency of the algorithm. The algorithm performance is further demonstrated using data of a terminal from Beijing Capital International Airport （PEK）.展开更多
An aeroelastic two-level optimization methodology for preliminary design of wing struc- tures is presented, in which the parameters for structural layout and sizes are taken as design vari- ables in the first-level op...An aeroelastic two-level optimization methodology for preliminary design of wing struc- tures is presented, in which the parameters for structural layout and sizes are taken as design vari- ables in the first-level optimization, and robust constraints in conjunction with conventional aeroelastic constraints are considered in the second-level optimization. A low-order panel method is used for aerodynamic analysis in the first-level optimization, and a high-order panel method is employed in the second-level optimization. It is concluded that the design of the abovementioned structural parameters of a wing can be improved using the present method with high efficiency. An improvement is seen in aeroelastic performance of the wing obtained with the present method when compared to the initial wing. Since these optimized structures are obtained after consideration of aerodynamic and structural uncertainties, they are well suited to encounter these uncertainties when they occur in reality.展开更多
Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensiv...Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression（PR）, neural networks（NN） and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression（SVR） is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm （GA）. The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks（BPNN）, the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure.展开更多
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
基金co-supported by the National Natural Science Foundation of China(Nos.62473110,62403166)the Fundamental Research Funds for the Central Universities,China(No.2023FRFK02043)+1 种基金the Natural Science Foundation of Heilongjiang Province,China(No.LH2022F023)the National Key Laboratory of Space Intelligent Control Foundation,China(No.2023-JCJQ-LB-006-19)。
文摘With the increasing number of geosynchronous orbit satellites with expiring lifetime,spacecraft refueling is crucial in enhancing the economic benefits of on-orbit services.The existing studies tend to be based on predetermined refueling duration;however,the precise mission scheduling solution will be difficult to apply due to uncertain refueling duration caused by orbital transfer deviations and stochastic actuator faults during actual on-orbit service.Therefore,this paper proposes a robust mission scheduling strategy for geosynchronous orbit spacecraft on-orbit refueling missions with uncertain refueling duration.Firstly,a robust mission scheduling model is constructed by introducing the budget uncertainty set to describe the uncertain refueling duration.Secondly,a hybrid harris hawks optimization algorithm is designed to explore the optimal mission allocation and refueling sequences,which combines cubic chaotic mapping to initialize the population,and the crossover in the genetic algorithm is introduced to enhance global convergence.Finally,the typical simulation examples are constructed with real-mission scenarios in three aspects to analyze:performance comparisons with various algorithms;robustness analyses via comparisons of different on-orbit refueling durations;investigations into the impacts of different initial population strategies on algorithm performance,demonstrating the proposed mission scheduling framework's robustness and effectiveness by comparing it with the exact mission scheduling.
基金Supported by the National Natural Science Foundation of China(61503384,61603393)Natural Science Foundation of Jiangsu(BK20150199,BK20160275)+1 种基金the Foundation Research Funds for the Central Universities(2015QNA65)the Postdoctoral Foundation of Jiangsu Province(1501081B)
文摘Although industrial processes often perform perfectly under design conditions, they may deviate from the optimal operating point owing to parameters drift, environmental disturbances, etc. Thus, it is necessary to develop efficacious strategies or procedure to assess the process performance online. In this paper, we explore the issue of operating optimality assessment for complex industrial processes based on performance-similarity considering nonlinearities and outliers simultaneously, and a general enforced online performance assessment framework is proposed. In the offline part, a new and modified total robust kernel projection to latent structures algorithm,T-KPRM, is proposed and used to evaluate the complex nonlinear industrial process, which can effectively extract the optimal-index-related process variation information from process data and establish assessment models for each performance grades overcoming the effects of outlier. In the online part, the online assessment results can be obtained by calculating the similarity between the online data from a sliding window and each of the performance grades. Furthermore, in order to improve the accuracy of online assessment, we propose an online assessment strategy taking account of the effects of noise and process uncertainties. The Euclidean distance between the sliding data window and the optimal evaluation level is employed to measure the contribution rates of variables, which indicate the possible reason for the non-optimal operating performance. The proposed framework is tested on a real industrial case: dense medium coal preparation process, and the results shows the efficiency of the proposed method comparing to the existing method.
基金supported by the National Natural Science Foundation of China(12401625)the China Postdoctoral Science Foundation(2024M753074)+1 种基金the Postdoctoral Fellowship Program of CPSF(GZC20232556)the Fundamental Research Funds for the Central Universities(WK2040000108).
文摘The Kelly strategy is a common approach in portfolio optimization problems that aims to maximize the expected portfolio growth rate in the long term.Its computation requires complete knowledge of the asset return distribution,which is obviously not observable,but can be inferred from sample data.Motivated by recent developments in data-driven optimization methods,we propose a new class of coherent Wasserstein data-driven Kelly portfolio optimization models.In particular,we establish a class of ambiguity sets based on coherent Wasserstein metrics,and these new metrics can strike a good balance between robustness and data-drivenness,thus providing richer choices for ambiguity set design.The Kelly portfolio optimization model,which is data-driven and based on coherent Wasserstein balls,can be solved efficiently as a finite-dimensional convex program.This model also provides a robust data-driven solution.In addition,we numerically investigate the proposed model and find that it outperforms the type-1 Wasserstein-Kelly portfolio,especially the classical Kelly portfolio.Moreover,it indicates that we can obtain a portfolio with higher final value and stability,especially in controlling volatility and maximum drawdown.
基金supported by the National Natural Science Foundation of China(62173136)the Natural Science Foundation of Hunan Province(2020JJ2013,2021JJ50047).
文摘Dear Editor,With the advances in computing and communication technologies,the cyber-physical system(CPS),has been used in lots of industrial fields,such as the urban water cycle,internet of things,and human-cyber systems[1],[2],which has to face up to malicious cyber-attacks towards cyber communication of control commands.Specifically,jamming attack is regarded as one of the most common attacks of decreasing network performance.Game theory is widely regarded as a method of accurately describing the interaction between jamming attacker and legitimate user[3].In the cyber layer,the signal game model has been utilized to describe the transmission between the attacker and defender[4].However,most previous game theoretical researches are not feasible to meet the demands of industrial CPSs mainly due to the shared communication network nature.Specifically,it leads to incomplete information for players of game owing to various network-induced phenomena and employed communication protocols.In the physical layer,the secure control[5]and estimation[6]under attack detection have been studied for CPSs.However,these methods not only rely heavily on signals injection detection,but also have no access to smart attackers who launch covert attacks so that data receivers cannot observe the attack behaviour[7].Accordingly,the motivation arising here is to tackle the nested game problem for CPSs subject to jamming attack.
基金supported by the National Natural Science Foundation of China(No.92471204)Youth Innovation Promotion Association,CAS,China。
文摘Trajectory planning under uncertain dynamics is critical for safety-critical systems like Unmanned Aerial Vehicles(UAVs),where uncertainties in aerodynamic force and control surface failure can lead to mission failure.This paper proposes a Multi-stage Robust Optimization(MRO)framework to address nonlinear trajectory planning with bounded but unknown parameters.By integrating first-order sensitivity analysis and sequential optimization,the proposed method ensures robustness against worst-case parameter deviations while maintaining high terminal accuracy.Unlike existing approaches,this paper explicitly quantifies uncertainty propagation through sensitivity bounds and divides long-term planning into sub-stages to reduce cumulative errors.Simulations on a UAV model with uncertainties in aerodynamic coefficients,wind fields and coefficients of control inputs demonstrate that MRO achieves high terminal state accuracy and strong robustness.
基金supported by National Key Research and Development Program(2024YFE0115600).
文摘Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainability through coordinated electricity,thermal,natural gas,and hydrogen utilization.This study proposes a two-stage distributionally robust optimization(DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES.The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response(DR)program to adjustflexible multi-energy loads,thereby prioritizing RES consumption.Uncertainties from RES generation and load demand are addressed through an ambiguity set,enabling robust decision-making.The column-and-constraint generation(C&CG)algorithm efficiently solves the two-stage DRO model.Case studies demonstrate that the proposed method reduces operational costs by 3.56%,increases photovoltaic consumption rates by 5.44%,and significantly lowers carbon emissions compared to conventional approaches.Furthermore,the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods,highlighting its potential to advance cost-effective,low-carbon energy systems while ensuring grid stability under uncertainty.
基金supported in part by the National Natural Science Foundation of China(NSFC)(92267205)the Natural Science Foundation of Hunan Province(2025JJ10007,2025JJ60423)the Open Research Project of the State Key Laboratory of Industrial Control Technology,China(ICT2024 B66).
文摘Most enterprises rely on railway transportation to deliver their products to customers,particularly in the salt lake chemical industry.Notably,allocating products to freight spaces and their assembly on transport vehicles are critical pre-transportation processes.However,due to demand fluctuations from changing product orders and unforeseen railway scheduling delays,manually adjusted allocation and loading may lead to excessive loading and unloading distances and times,ultimately increasing transportation costs for enterprises.To address these issues,this paper proposes a data-driven two-stage robust optimization(TSRO)framework embedding with the gated stacked temporal autoencoder clustering based on the attention mechanism(GSTAC-AM),which aims to overcome demand uncertainty and enhance the efficiency of freight allocation and loading.Specifically,GSTAC-AM is developed to help predict the deviation level of demand uncertainty and mitigate the impact of potential outliers.Then,a robust counterpart model is formulated to ensure computational tractability.In addition,a multi-stage hybrid heuristic algorithm is designed to handle the large scale and complexity inherent in the freight space allocation and loading processes.Finally,the effectiveness and applicability of the proposed framework are validated through a real case study conducted in a large salt lake chemical enterprise.
基金supported by the National Nature Science Foundation of China(Nos.62063019)Natural Science Foundation of Gansu Province(22JR5RA241,2023CXZX-465).
文摘In this study,we construct a bi-level optimization model based on the Stackelberg game and propose a robust optimization algorithm for solving the bi-level model,assuming an actual situation with several participants in energy trading.Firstly,the energy trading process is analyzed between each subject based on the establishment of the operation framework of multi-agent participation in energy trading.Secondly,the optimal operation model of each energy trading agent is established to develop a bi-level game model including each energy participant.Finally,a combination algorithm of improved robust optimization over time(ROOT)and CPLEX is proposed to solve the established game model.The experimental results indicate that under different fitness thresholds,the robust optimization results of the proposed algorithm are increased by 56.91%and 68.54%,respectively.The established bi-level game model effectively balances the benefits of different energy trading entities.The proposed algorithm proposed can increase the income of each participant in the game by an average of 8.59%.
文摘Trapped ion hardware has made significant progress recently and is now one of the leading platforms for quantum computing.To construct two-qubit gates in trapped ions,experimentalmanipulation approaches for ion chains are becoming increasingly prevalent.Given the restricted control technology,how implementing high-fidelity quantum gate operations is crucial.Many works in current pulse design optimization focus on ion–phonon and effective ion–ion couplings while ignoring the first-order derivative terms expansion impacts of these two terms brought on by experiment defects.This paper proposes a novel robust quantum control optimization method in trapped ions.By introducing the first-order derivative terms caused by the error into the optimization cost function,we generate an extremely robust Mølmer–Sørensen gate with infidelity below 10^(−3) under a drift noise range of±10 kHz,the relative robustness achieves a tolerance of±5%,compared to the 200-kHz frequency spacing between phonon modes,and for time noise drift,the tolerance reached to 2%.Our work reveals the vital role of the first-order derivative terms of coupling in trapped ion pulse control optimization,especially the first-order derivative terms of ion–ion coupling.It provides a robust optimization scheme for realizing more efficient entangled states in trapped ion platforms.
基金Supported by the National Natural Science Foundation of China(No.U24B20156)the National Defense Basic Scientific Research Program of China(No.JCKY2021204B051)the National Laboratory of Space Intelligent Control of China(Nos.HTKJ2023KL502005 and HTKJ2024KL502007)。
文摘A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.
基金supported by the National Natural Science Foundation of China(Grant Nos.12441502,12122506,12204230,and 12404554)the National Science and Technology Major Project of the Ministry of Science and Technology of China(2024ZD0300404)+6 种基金Guangdong Basic and Applied Basic Research Foundation(Grant No.2021B1515020070)Shenzhen Science and Technology Program(Grant No.RCYX20200714114522109)China Postdoctoral Science Foundation(CPSF)(2024M762114)Postdoctoral Fellowship Program of CPSF(GZC20231727)supported by the National Natural Science Foundation of China(Grant Nos.92165206 and 11974330)Innovation Program for Quantum Science and Technology(Grant No.2021ZD0301603)the Fundamental Research Funds for the Central Universities。
文摘We present a robust quantum optimal control framework for implementing fast entangling gates on ion-trap quantum processors.The framework leverages tailored laser pulses to drive the multiple vibrational sidebands of the ions to create phonon-mediated entangling gates and,unlike the state of the art,requires neither weakcoupling Lamb-Dicke approximation nor perturbation treatment.With the application of gradient-based optimal control,it enables finding amplitude-and phase-modulated laser control protocols that work without the Lamb-Dicke approximation,promising gate speeds on the order of microseconds comparable to the characteristic trap frequencies.Also,robustness requirements on the temperature of the ions and initial optical phase can be conveniently included to pursue high-quality fast gates against experimental imperfections.Our approach represents a step in speeding up quantum gates to achieve larger quantum circuits for quantum computation and simulation,and thus can find applications in near-future experiments.
基金supported by the Key Project of Shanghai(Project Number A1-0224-25-002-02-040,Municipal Key Course—Heat Transfer)funded by the National Natural Science Foundation of China(Grant No.52077137).
文摘To enhance the low-carbon economic efficiency and increase the utilization of renewable energy within integrated energy systems(IES),this paper proposes a low-carbon dispatch model integrating power-to-gas(P2G),carbon capture and storage(CCS),hydrogen fuel cell(HFC),and combined heat and power(CHP).The P2G process is refined into a two-stage structure,and HFC is introduced to enhance hydrogen utilization.Together with CCS and CHP,these devices form a multi-energy conversion system coupling electricity,heat,cooling,and gas.A laddertype carbon trading approach is adopted to flexibly manage carbon output by leveraging marginal cost adjustments.To evaluate the resilience of the proposed low-carbon scheduling strategy involving multiple energy units under the variability of renewable energy,a two-level robust optimization framework is developed.This model captures the most adverse scenarios of wind and solar generation.The dispatch strategy is validated against these extreme conditions to demonstrate its flexibility and effectiveness.The problem is solved using the GUROBI optimization tool.Results from simulations indicate that themodel increases renewable energy integration by 39.1%,and achieves reductions of 15.96%in carbon emissions and 16.29%in operational expenditures.The results demonstrate that the strategy ensures both economic efficiency and environmental performance under uncertain conditions.Compared with existing studies that separately model two-stage P2G or CCS devices,this paper integrates HFC,CHP,and CCS into a unified dispatchable system,enabling refined hydrogen utilization and flexible carbon circulation.Furthermore,the introduction of a laddertype carbon pricing mechanism,combined with multi-energy storage participation in implicit demand response,creates a dynamic and cost-sensitive dispatch framework.These modeling strategies go beyond conventional linear IES formulations and provide more realistic system representations.The proposed approach not only deepens the coupling among electric,thermal,and gas systems,but also offers a feasible pathway for high-penetration renewable integration in low-carbon energy systems.
基金supported by the National Natural Science Foundation of China(72571094,72271076,71871079)。
文摘Efficient multiple unmanned aerial vehicles(UAVs)path planning is crucial for improving mission completion efficiency in UAV operations.However,during the actual flight of UAVs,the flight time between nodes is always influenced by external factors,making the original path planning solution ineffective.In this paper,the multi-depot multi-UAV path planning problem with uncertain flight time is modeled as a robust optimization model with a budget uncertainty set.Then,the robust optimization model is transformed into a mixed integer linear programming model by the strong duality theorem,which makes the problem easy to solve.To effectively solve large-scale instances,a simulated annealing algorithm with a robust feasibility check(SA-RFC)is developed.The numerical experiment shows that the SA-RFC can find high-quality solutions within a few seconds.Moreover,the effect of the task location distribution,depot counts,and variations in robustness parameters on the robust optimization solution is analyzed by using Monte Carlo experiments.The results demonstrate that the proposed robust model can effectively reduce the risk of the UAV failing to return to the depot without significantly compromising the profit.
基金supported the National Natural Science Foundation of China (71621001, 71825004, and 72001019)the Fundamental Research Funds for Central Universities (2020JBM031 and 2021YJS203)the Research Foundation of State Key Laboratory of Rail Traffic Control and Safety (RCS2020ZT001)
文摘Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous congestion and further reduce the risk of cross-infection,a novel two-stage distributionally robust optimization(DRO)model is explicitly constructed,in which the probability distribution of stochastic scenarios is only partially known in advance.In the proposed model,the mean-conditional value-at-risk(CVaR)criterion is employed to obtain a tradeoff between the expected number of waiting passen-gers and the risk of congestion on an urban rail transit line.The relationship between the proposed DRO model and the traditional two-stage stochastic programming(SP)model is also depicted.Furthermore,to overcome the obstacle of model solvability resulting from imprecise probability distributions,a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form.A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming(MILP)solver is developed to improve the computational efficiency of large-scale instances.Finally,a series of numerical examples with real-world operation data are executed to validate the pro-posed approaches.
基金Supported by National Natural Science Foundation of China(Grant No.51406148)National Science Technology Support Program of China(Grant No.2012BAA08B06)Postdoctoral Scientific Foundation of China(Grant No.2014M552444)
文摘Blade fouling has been proved to be a great threat to compressor performance in operating stage.The current researches on fouling-induced performance degradations of centrifugal compressors are based mainly on simplified roughness models without taking into account the realistic factors such as spatial non-uniformity and randomness of the fouling-induced surface roughness.Moreover,little attention has been paid to the robust design optimization of centrifugal compressor impellers with considerations of blade fouling.In this paper,a multi-objective robust design optimization method is developed for centrifugal impellers under surface roughness uncertainties due to blade fouling.A three-dimensional surface roughness map is proposed to describe the nonuniformity and randomness of realistic fouling accumulations on blades.To lower computational cost in robust design optimization,the support vector regression(SVR)metamodel is combined with the Monte Carlo simulation(MCS)method to conduct the uncertainty analysis of fouled impeller performance.The analyzed results show that the critical fouled region associated with impeller performance degradations lies at the leading edge of blade tip.The SVR metamodel has been proved to be an efficient and accurate means in the detection of impeller performance variations caused by roughness uncertainties.After design optimization,the robust optimal design is found to be more efficient and less sensitive to fouling uncertainties while maintaining good impeller performance in the clean condition.This research proposes a systematic design optimization method for centrifugal compressors with considerations of blade fouling,providing a practical guidance to the design of advanced centrifugal compressors.
基金co-supported by the Aeronautical Science Foundation of China(No.2016ZA51003)Aerospace Science and Technology Foundation of China(No.2017129001)+1 种基金National Key R&D Program of China(No.2016YFB1200100)National Natural Science Foundation of China(No.11502267)
文摘This paper examines robust optimization design and analysis of a conformal expansion nozzle of flying wing Unmanned Aerial Vehicle （UAV） with the inverse-design idea. In view of flow features and stealth constraints, the inverse-design idea is described and the uncertainty-based robust design model is presented. A robust design system employs this model to combine deterministic optimization and robust optimization and is applied into design of a conformal expansion nozzle. The results indicate that design optimization can conform to the anticipation of the inversedesign idea and significantly improve the aerodynamic performance that meet the requirement of 6σ. The present method is a feasible nozzle design strategy that integrates robust optimization and inverse-design.
文摘This paper reviews existing approaches to the airport gate assignment problem （AGAP） and presents an optimization model for the problem considering operational safety constraints. The main objective is to minimize the dispersion of gate idle time periods （to get robust optimization） while ensuring appropriate matching between the size of each aircraft and its assigned gate type and avoiding the potential hazard caused by gate apron operational conflict. Genetic algorithm is adopted to solve the problem, An illustrative example is given to show the effectiveness and efficiency of the algorithm. The algorithm performance is further demonstrated using data of a terminal from Beijing Capital International Airport （PEK）.
基金supported by the National Natural Science Foundation of China (No. 11172025 and No. 91116005)
文摘An aeroelastic two-level optimization methodology for preliminary design of wing struc- tures is presented, in which the parameters for structural layout and sizes are taken as design vari- ables in the first-level optimization, and robust constraints in conjunction with conventional aeroelastic constraints are considered in the second-level optimization. A low-order panel method is used for aerodynamic analysis in the first-level optimization, and a high-order panel method is employed in the second-level optimization. It is concluded that the design of the abovementioned structural parameters of a wing can be improved using the present method with high efficiency. An improvement is seen in aeroelastic performance of the wing obtained with the present method when compared to the initial wing. Since these optimized structures are obtained after consideration of aerodynamic and structural uncertainties, they are well suited to encounter these uncertainties when they occur in reality.
基金supported by National Natural Science Foundation of China (Grant No.60572007)National Basic Research Program of China(973 Program,Grant No.613580202)
文摘Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression（PR）, neural networks（NN） and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression（SVR） is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm （GA）. The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks（BPNN）, the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure.
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.
