This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise variables.The focus...This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise variables.The focus is placed on the scenario that the dynamical state of the half-vehicle active suspension system is transmitted over an in-vehicle controller area network that only permits the transmission of sampled data packets.For this purpose,a stochastic sampling mechanism is developed such that the sampling periods can randomly switch among different values with certain mathematical probabilities.Then,an asynchronous fuzzy sampled-data controller,featuring distinct premise variables from the active suspension system,is constructed to eliminate the stringent requirement that the sampled-data controller has to share the same grades of membership.Furthermore,novel criteria for both stability analysis and controller design are derived in order to guarantee that the resultant closed-loop active suspension system is stochastically stable with simultaneous𝐻2 and𝐻∞performance requirements.Finally,the effectiveness of the proposed stochastic sampled-data multi-objective control method is verified via several numerical cases studies in both time domain and frequency domain under various road disturbance profiles.展开更多
The aircraft engine multi-loop control system is described and the switching control theory is introduced to solve the regulating and protecting control problems in this paper. The aircraft engine multi-loop control s...The aircraft engine multi-loop control system is described and the switching control theory is introduced to solve the regulating and protecting control problems in this paper. The aircraft engine multi-loop control system is firstly described and the control problems are formulated. Secondly, the theory of the smooth switching control is devoted and a new extended scheme for the smooth switching of a switched control system is introduced. Then, for the key technologies of aero-engines switching control, a design algorithm is presented which can determine which candidate controller should be put in feedback with the plant to achieve a desired performance and the procedure to design the aircraft engine multi-loop control system is detailed. The switching performance objectives and the switching scheme are given and a family of PID controllers and compensators is designed. The simulation shows that using the switching control design method can not only improve the dynamic performance of the aircraft engine control system and reduce the switching times, but also guarantee the stability in some peculiar occasions.展开更多
This paper proposes a new approach for multi-objective robust control. The approach extends the standard generalized l2 (Gl2) and generalized H2 (GH2) conditions to a set of new linear matrix inequality (LMI) constra...This paper proposes a new approach for multi-objective robust control. The approach extends the standard generalized l2 (Gl2) and generalized H2 (GH2) conditions to a set of new linear matrix inequality (LMI) constraints based on a new stability condition. A technique for variable parameterization is introduced to the multi-objective control problem to preserve the linearity of the synthesis variables. Consequently, the multi-channel multi-objective mixed Gl2/GH2 control problem can be solved less conservatively using computationally tractable algorithms developed in the paper.展开更多
Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev...Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.展开更多
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu...Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.展开更多
In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and red...In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.展开更多
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address t...Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method f...Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.展开更多
Control of combustion stability in partial pre-mixed compression ignition(PPCI)engine is one of the main issues facing its application.However,the multi-parameter coupling and nonlinear increase in the combustion proc...Control of combustion stability in partial pre-mixed compression ignition(PPCI)engine is one of the main issues facing its application.However,the multi-parameter coupling and nonlinear increase in the combustion process make the model and controller design more difficult.Therefore,this study proposed a diesel engine control method that combines neural networks and model-free adaptive control in the absence of model and controller structure,which can achieve real-time coordination control of crank angle at 50%of total heat release(CA50)and indicated mean effective pressure(IMEP)in the PPCI combustion process.Through comparisons under different operating conditions,it was found that the adjustment of algorithm parameters needs to adapt to the sensitivity changes of control parameters.In addition,the study validated the real-time performance and control effect of the algorithm,the experimental results indicate that the execution time of the control algorithm is approximately 5.59 milliseconds,which satisfies the real-time control requirements for the combustion process.By adjusting the weight coefficient matrix of the control authority,CA50 and IMEP are effectively tracked within the constraints of maximum pressure rise rate.The control error for CA50 remains within±2.7%,while that for IMEP is confined to±1%.Furthermore,the root mean square error for CA50 is measured at 1.1 crank angle,and for IMEP it stands at 23.5 kPa,thereby achieving precise real-time control of the PPCI combustion process.展开更多
This review article provides a comprehensive analysis of the latest advancements and persistent challenges in Software-Defined Wide Area Networks(SD-WANs),with a particular emphasis on the multi-objective Controller P...This review article provides a comprehensive analysis of the latest advancements and persistent challenges in Software-Defined Wide Area Networks(SD-WANs),with a particular emphasis on the multi-objective Controller Placement Problem(CPP).As SD-WAN technology continues to gain prominence for its capacity to offer flexible and efficient network management,the task of 36optimally placing controllers—responsible for orchestrating and managing network traffic—remains a critical yet complex challenge.This review delves into recent innovations in multi-objective controller placement strategies,including clustering techniques,heuristic-based approaches,and the integration of machine learning and deep learning models.Each methodology is critically evaluated in terms of its ability to minimize network latency,enhance fault tolerance,and improve overall network performance.Furthermore,this paper discusses the inherent limitations and challenges associated with these techniques,providing a critical evaluation of their current utility and outlining potential avenues for future research.By offering a thorough overview of state-of-the-art approaches to multi-objective controller placement in SD-WANs,this review aims to inform ongoing advancements and highlight emerging research opportunities in this evolving field.展开更多
In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelli...In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multiobjective optimization(MOO).First,the effective TBM operation dataset is obtained through data preprocessing of the Songhua River(YS)tunnel project in China.Next,the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters(i.e.total thrust and cutterhead torque),rock mass classification,and hazard risks(i.e.tunnel collapse and shield jamming).Then,considering three optimal objectives,(i.e.,penetration rate,rock-breaking energy consumption,and cutterhead hob wear),the MOO framework and corresponding mathematical expression are established.The Pareto optimal front is solved using DE-NSGA-II algorithm.Finally,the optimal control parameters(i.e.,advance rate and cutterhead rotation speed)are obtained by the satisfactory solution determination criterion,which can balance construction safety and efficiency with satisfaction.Furthermore,the proposed method is validated through 50 cases of TBM tunnelling,showing promising potential of application.展开更多
Dear Editor,This letter proposes a novel Nash bargaining solution-based multiobjective model predictive control(MPC)scheme to deal with the interaction force control and the path-following problem of the constrained i...Dear Editor,This letter proposes a novel Nash bargaining solution-based multiobjective model predictive control(MPC)scheme to deal with the interaction force control and the path-following problem of the constrained interactive robot.Considering the elastic interaction force model,a mechanical trade-off always exists between the interaction force and position,which means that neither force nor path following can satisfy their desired demands completely.Based on this consideration,two irreconcilable control specifications,the force object function and the position track object function,are proposed,and a new multi-objective MPC scheme is then designed.展开更多
Compared to other energy sources,nuclear reactors offer several advantages as a spacecraft power source,including compact size,high power density,and long operating life.These qualities make nuclear power an ideal ene...Compared to other energy sources,nuclear reactors offer several advantages as a spacecraft power source,including compact size,high power density,and long operating life.These qualities make nuclear power an ideal energy source for future deep space exploration.A whole system model of the space nuclear reactor consisting of the reactor neutron kinetics,reactivity control,reactor heat transfer,heat exchanger,and thermoelectric converter was developed.In addition,an electrical power control system was designed based on the developed dynamic model.The GRS method was used to quantitatively calculate the uncertainty of coupling parameters of the neutronics,thermal-hydraulics,and control system for the space reactor.The Spearman correlation coefficient was applied in the sensitivity analysis of system input parameters to output parameters.The calculation results showed that the uncertainty of the output parameters caused by coupling parameters had the most considerable variation,with a relative standard deviation<2.01%.Effective delayed neutron fraction was most sensitive to electrical power.To obtain optimal control performance,the non-dominated sorting genetic algorithm method was employed to optimize the controller parameters based on the uncertainty quantification calculation.Two typical transient simulations were conducted to test the adaptive ability of the optimized controller in the uncertainty dynamic system,including 100%full power(FP)to 90%FP step load reduction transient and 5%FP/min linear variable load transient.The results showed that,considering the influence of system uncertainty,the optimized controller could improve the response speed and load following accuracy of electrical power control,in which the effectiveness and superiority have been verified.展开更多
This paper proposes a switching multi-objective model predictive control(MOMPC) algorithm for constrained nonlinear continuous-time process systems.Different cost functions to be minimized in MPC are switched to satis...This paper proposes a switching multi-objective model predictive control(MOMPC) algorithm for constrained nonlinear continuous-time process systems.Different cost functions to be minimized in MPC are switched to satisfy different performance criteria imposed at different sampling times.In order to ensure recursive feasibility of the switching MOMPC and stability of the resulted closed-loop system,the dual-mode control method is used to design the switching MOMPC controller.In this method,a local control law with some free-parameters is constructed using the control Lyapunov function technique to enlarge the terminal state set of MOMPC.The correction term is computed if the states are out of the terminal set and the free-parameters of the local control law are computed if the states are in the terminal set.The recursive feasibility of the MOMPC and stability of the resulted closed-loop system are established in the presence of constraints and arbitrary switches between cost functions.Finally,implementation of the switching MOMPC controller is demonstrated with a chemical process example for the continuous stirred tank reactor.展开更多
Variable-speed wind power generation system usually adopts maximum power point tracking(MPPT)below the rated wind speed or constant power control above the rated wind speed.However,single objective control has large d...Variable-speed wind power generation system usually adopts maximum power point tracking(MPPT)below the rated wind speed or constant power control above the rated wind speed.However,single objective control has large dynamic loads on transmission systems.This paper presents a multi-objective control scheme for wind turbine in low wind speed,which achieves not only high power capture efficiency but also low fatigue load on mechanical parts.Input wind speed is separated into two components:a slowly varying seasonal component and a rapidly varying turbulent component.Correspondingly,the machine-side converter controller is divided into low frequency loop and high frequency loops.The low frequency loop implements MPPT control,the high frequency loop reduces the fluctuations of torque,and both loops together suppress the variations of output power.Simulation was conducted in Matlab/Simulink and FAST code was used to further verify the correctness of the multi-objective control strategy.展开更多
Rollover and jack-knifing of tractor semi-trailer are serious threats for vehicle safety, and accordingly active safety technologies have been widely used to reduce or prevent the occurrence of such accidents. However...Rollover and jack-knifing of tractor semi-trailer are serious threats for vehicle safety, and accordingly active safety technologies have been widely used to reduce or prevent the occurrence of such accidents. However, currently tractor semi-trailer stability control is generally only a single hazardous condition (rollover or jack-knifing) control, it is difficult to ensure the vehicle comprehensive stability of various dangerous conditions. The main objective of this study is to introduce a multi-objective stability control algorithm which can improve the vehicle stability of a tractor semi-trailer by using differential braking. A vehicle controller is designed to minimize the likelihood of rollover and jack-knifing. First a linear vehicle model of tractor semi-trailer is constructed. Then an optimal yaw control for tractor using differential braking is applied to minimize the yaw rate and lateral acceleration deviation of tractor, as well as the hitch articulation angle of tractor semi-trailer, so as to improve the vehicle stability. Second a braking scheme and variable structure control with sliding mode control are introduced in order to achieve the best braking effect. Last Fishhook maneuver is introduced to the active safety simulation and the active control system effect verification. The simulation results show that multi-objective stability control algorithm of semi-trailer could improve the vehicle stability significantly during the transient maneuvers. The proposed multi-objective stability control algorithm is effective to prevent the vehicle rollover and jackknifing.展开更多
For automated vehicles,comfortable driving will improve passengers’ satisfaction.Reducing fuel consumption brings economic profits for car owners,decreases the impact on the environment and increases energy sustainab...For automated vehicles,comfortable driving will improve passengers’ satisfaction.Reducing fuel consumption brings economic profits for car owners,decreases the impact on the environment and increases energy sustainability.In addition to comfort and fuel-economy,automated vehicles also have the basic requirements of safety and car-following.For this purpose,an adaptive cruise control (ACC) algorithm with multi-objectives is proposed based on a model predictive control (MPC) framework.In the proposed ACC algorithm,safety is guaranteed by constraining the inter-distance within a safe range; the requirements of comfort and car-following are considered to be the performance criteria and some optimal reference trajectories are introduced to increase fuel-economy.The performances of the proposed ACC algorithm are simulated and analyzed in five representative traffic scenarios and multiple experiments.The results show that not only are safety and car-following objectives satisfied,but also driving comfort and fuel-economy are improved significantly.展开更多
For different flight phases in an overall flight mission,different control and allocation preferences should be pursued considering lift,drag or maneuverability characteristics.The multi-objective flight control alloc...For different flight phases in an overall flight mission,different control and allocation preferences should be pursued considering lift,drag or maneuverability characteristics.The multi-objective flight control allocation problem for a multi-phase flight mission is studied.For an overall flight mission,different flight phases namely climbing,cruise,maneuver and gliding phases are defined.Firstly,a multi-objective control allocation problem considering drag,lift or control energy preference is constructed.Secondly,considering different control preferences at different flight phases,the analytic hierarchical process method is used to construct a comprehensive performance index from different objectives such as lift or drag preferences.The active set based dynamic programming optimization method is used to solve the real-time optimization problem.For the validation,the Innovative Control Effector(ICE)tailless aircraft nonlinear model and the angular acceleration measurements based adaptive Incremental Backstepping(IBKS)are used to construct the validation platform.Finally,an overall flight mission is simulated to demonstrate the efficiency of the proposed multi-phase and multi-objective flight control allocation method.The results show that the comprehensive performance index for different phases,which are determined from the Analytic Hierarchy Process(AHP)method,can suitably satisfy the preference requirements for different flight phases.展开更多
文摘This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise variables.The focus is placed on the scenario that the dynamical state of the half-vehicle active suspension system is transmitted over an in-vehicle controller area network that only permits the transmission of sampled data packets.For this purpose,a stochastic sampling mechanism is developed such that the sampling periods can randomly switch among different values with certain mathematical probabilities.Then,an asynchronous fuzzy sampled-data controller,featuring distinct premise variables from the active suspension system,is constructed to eliminate the stringent requirement that the sampled-data controller has to share the same grades of membership.Furthermore,novel criteria for both stability analysis and controller design are derived in order to guarantee that the resultant closed-loop active suspension system is stochastically stable with simultaneous𝐻2 and𝐻∞performance requirements.Finally,the effectiveness of the proposed stochastic sampled-data multi-objective control method is verified via several numerical cases studies in both time domain and frequency domain under various road disturbance profiles.
基金supported by the National Natural Science Foundation of China (Grant No. 61104146/F030203)Innovation Plan of Aero Engine Complex System Safety by the Ministry of Education Chang Jiang Scholars of China (Grant No. IRT0905)
文摘The aircraft engine multi-loop control system is described and the switching control theory is introduced to solve the regulating and protecting control problems in this paper. The aircraft engine multi-loop control system is firstly described and the control problems are formulated. Secondly, the theory of the smooth switching control is devoted and a new extended scheme for the smooth switching of a switched control system is introduced. Then, for the key technologies of aero-engines switching control, a design algorithm is presented which can determine which candidate controller should be put in feedback with the plant to achieve a desired performance and the procedure to design the aircraft engine multi-loop control system is detailed. The switching performance objectives and the switching scheme are given and a family of PID controllers and compensators is designed. The simulation shows that using the switching control design method can not only improve the dynamic performance of the aircraft engine control system and reduce the switching times, but also guarantee the stability in some peculiar occasions.
基金Project supported by the National Natural Science Foundation ofChina (No. 60374028) and the Scientific Research Foundation forReturned Overseas Chinese Scholars Ministry of Education (No.[2004]176)
文摘This paper proposes a new approach for multi-objective robust control. The approach extends the standard generalized l2 (Gl2) and generalized H2 (GH2) conditions to a set of new linear matrix inequality (LMI) constraints based on a new stability condition. A technique for variable parameterization is introduced to the multi-objective control problem to preserve the linearity of the synthesis variables. Consequently, the multi-channel multi-objective mixed Gl2/GH2 control problem can be solved less conservatively using computationally tractable algorithms developed in the paper.
文摘Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures.
基金supported by the National Natural Science Foundation of China(No.12202295)the International(Regional)Cooperation and Exchange Projects of the National Natural Science Foundation of China(No.W2421002)+2 种基金the Sichuan Science and Technology Program(No.2025ZNSFSC0845)Zhejiang Provincial Natural Science Foundation of China(No.ZCLZ24A0201)the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.GK249909299001-004)。
文摘Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:https://github.com/jinhao-ok1/Topo-for-DCTWS.git.
基金supported by the National Natural Science Foundation of China under Grant No.61972040the Science and Technology Research and Development Project funded by China Railway Material Trade Group Luban Company.
文摘In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs.
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
基金supported by the Fundamental Research Funds for the Provincial Universities (Grant No.2024-KYYWF-0141)the National Natural Science Foundation of China (Grant Nos.52406076,52227813)+1 种基金the National Key Research and Development Program of China (Grant No.2022YFE0133900)the China Postdoctoral Science Foundation (Grant No.2023M740905)。
文摘Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金National Natural Science Foundation of China,No.42301470,No.52270185,No.42171389Capacity Building Program of Local Colleges and Universities in Shanghai,No.21010503300。
文摘Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.
基金supported by the Key Laboratory for Power Ma-chinery&Engineering of the State Education Ministry,Shanghai Jiao Tong Universitythe Research on Intelligent Control and Condition-Based Maintenance Technology for Marine Low-Speed Engines(Grant No.520-KT04)for grants and supports.
文摘Control of combustion stability in partial pre-mixed compression ignition(PPCI)engine is one of the main issues facing its application.However,the multi-parameter coupling and nonlinear increase in the combustion process make the model and controller design more difficult.Therefore,this study proposed a diesel engine control method that combines neural networks and model-free adaptive control in the absence of model and controller structure,which can achieve real-time coordination control of crank angle at 50%of total heat release(CA50)and indicated mean effective pressure(IMEP)in the PPCI combustion process.Through comparisons under different operating conditions,it was found that the adjustment of algorithm parameters needs to adapt to the sensitivity changes of control parameters.In addition,the study validated the real-time performance and control effect of the algorithm,the experimental results indicate that the execution time of the control algorithm is approximately 5.59 milliseconds,which satisfies the real-time control requirements for the combustion process.By adjusting the weight coefficient matrix of the control authority,CA50 and IMEP are effectively tracked within the constraints of maximum pressure rise rate.The control error for CA50 remains within±2.7%,while that for IMEP is confined to±1%.Furthermore,the root mean square error for CA50 is measured at 1.1 crank angle,and for IMEP it stands at 23.5 kPa,thereby achieving precise real-time control of the PPCI combustion process.
文摘This review article provides a comprehensive analysis of the latest advancements and persistent challenges in Software-Defined Wide Area Networks(SD-WANs),with a particular emphasis on the multi-objective Controller Placement Problem(CPP).As SD-WAN technology continues to gain prominence for its capacity to offer flexible and efficient network management,the task of 36optimally placing controllers—responsible for orchestrating and managing network traffic—remains a critical yet complex challenge.This review delves into recent innovations in multi-objective controller placement strategies,including clustering techniques,heuristic-based approaches,and the integration of machine learning and deep learning models.Each methodology is critically evaluated in terms of its ability to minimize network latency,enhance fault tolerance,and improve overall network performance.Furthermore,this paper discusses the inherent limitations and challenges associated with these techniques,providing a critical evaluation of their current utility and outlining potential avenues for future research.By offering a thorough overview of state-of-the-art approaches to multi-objective controller placement in SD-WANs,this review aims to inform ongoing advancements and highlight emerging research opportunities in this evolving field.
基金supported by the National Natural Science Foundation of China(Grant No.52179105)China Postdoctoral Science Foundation(Grant No.2024M762193)。
文摘In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multiobjective optimization(MOO).First,the effective TBM operation dataset is obtained through data preprocessing of the Songhua River(YS)tunnel project in China.Next,the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters(i.e.total thrust and cutterhead torque),rock mass classification,and hazard risks(i.e.tunnel collapse and shield jamming).Then,considering three optimal objectives,(i.e.,penetration rate,rock-breaking energy consumption,and cutterhead hob wear),the MOO framework and corresponding mathematical expression are established.The Pareto optimal front is solved using DE-NSGA-II algorithm.Finally,the optimal control parameters(i.e.,advance rate and cutterhead rotation speed)are obtained by the satisfactory solution determination criterion,which can balance construction safety and efficiency with satisfaction.Furthermore,the proposed method is validated through 50 cases of TBM tunnelling,showing promising potential of application.
基金supported by the National Natural Science Foundation of China(62303095)the Natural Science Foundation of Sichuan Province(2023NSFSC0872).
文摘Dear Editor,This letter proposes a novel Nash bargaining solution-based multiobjective model predictive control(MPC)scheme to deal with the interaction force control and the path-following problem of the constrained interactive robot.Considering the elastic interaction force model,a mechanical trade-off always exists between the interaction force and position,which means that neither force nor path following can satisfy their desired demands completely.Based on this consideration,two irreconcilable control specifications,the force object function and the position track object function,are proposed,and a new multi-objective MPC scheme is then designed.
基金supported by the National Natural Science Foundation of China(12305185)Natural Science Foundation of Hunan Province,China(No.2023JJ50122)+1 种基金International Cooperative Research Project of the Ministry of Education,China(No.HZKY20220355)Scientific Research Foundation of the Education Department of Hunan Province,China(No.22A0307).
文摘Compared to other energy sources,nuclear reactors offer several advantages as a spacecraft power source,including compact size,high power density,and long operating life.These qualities make nuclear power an ideal energy source for future deep space exploration.A whole system model of the space nuclear reactor consisting of the reactor neutron kinetics,reactivity control,reactor heat transfer,heat exchanger,and thermoelectric converter was developed.In addition,an electrical power control system was designed based on the developed dynamic model.The GRS method was used to quantitatively calculate the uncertainty of coupling parameters of the neutronics,thermal-hydraulics,and control system for the space reactor.The Spearman correlation coefficient was applied in the sensitivity analysis of system input parameters to output parameters.The calculation results showed that the uncertainty of the output parameters caused by coupling parameters had the most considerable variation,with a relative standard deviation<2.01%.Effective delayed neutron fraction was most sensitive to electrical power.To obtain optimal control performance,the non-dominated sorting genetic algorithm method was employed to optimize the controller parameters based on the uncertainty quantification calculation.Two typical transient simulations were conducted to test the adaptive ability of the optimized controller in the uncertainty dynamic system,including 100%full power(FP)to 90%FP step load reduction transient and 5%FP/min linear variable load transient.The results showed that,considering the influence of system uncertainty,the optimized controller could improve the response speed and load following accuracy of electrical power control,in which the effectiveness and superiority have been verified.
基金Supported by the National Natural Science Foundation of China(61374111)the Natural Science Foundation of Zhejiang Province(LY13F030006)Agricultural Key Program of Ningbo City(2014C10068)
文摘This paper proposes a switching multi-objective model predictive control(MOMPC) algorithm for constrained nonlinear continuous-time process systems.Different cost functions to be minimized in MPC are switched to satisfy different performance criteria imposed at different sampling times.In order to ensure recursive feasibility of the switching MOMPC and stability of the resulted closed-loop system,the dual-mode control method is used to design the switching MOMPC controller.In this method,a local control law with some free-parameters is constructed using the control Lyapunov function technique to enlarge the terminal state set of MOMPC.The correction term is computed if the states are out of the terminal set and the free-parameters of the local control law are computed if the states are in the terminal set.The recursive feasibility of the MOMPC and stability of the resulted closed-loop system are established in the presence of constraints and arbitrary switches between cost functions.Finally,implementation of the switching MOMPC controller is demonstrated with a chemical process example for the continuous stirred tank reactor.
基金Supported by the National Natural Science Foundation of China under Grants 51320105002 and 51577025.
文摘Variable-speed wind power generation system usually adopts maximum power point tracking(MPPT)below the rated wind speed or constant power control above the rated wind speed.However,single objective control has large dynamic loads on transmission systems.This paper presents a multi-objective control scheme for wind turbine in low wind speed,which achieves not only high power capture efficiency but also low fatigue load on mechanical parts.Input wind speed is separated into two components:a slowly varying seasonal component and a rapidly varying turbulent component.Correspondingly,the machine-side converter controller is divided into low frequency loop and high frequency loops.The low frequency loop implements MPPT control,the high frequency loop reduces the fluctuations of torque,and both loops together suppress the variations of output power.Simulation was conducted in Matlab/Simulink and FAST code was used to further verify the correctness of the multi-objective control strategy.
基金supported by Open Research Fund of State Key Laboratory of Automobile Dynamics Simulation, China (Grant No. 20101103)National Natural Science Foundation of China (Grant No. 51075176)
文摘Rollover and jack-knifing of tractor semi-trailer are serious threats for vehicle safety, and accordingly active safety technologies have been widely used to reduce or prevent the occurrence of such accidents. However, currently tractor semi-trailer stability control is generally only a single hazardous condition (rollover or jack-knifing) control, it is difficult to ensure the vehicle comprehensive stability of various dangerous conditions. The main objective of this study is to introduce a multi-objective stability control algorithm which can improve the vehicle stability of a tractor semi-trailer by using differential braking. A vehicle controller is designed to minimize the likelihood of rollover and jack-knifing. First a linear vehicle model of tractor semi-trailer is constructed. Then an optimal yaw control for tractor using differential braking is applied to minimize the yaw rate and lateral acceleration deviation of tractor, as well as the hitch articulation angle of tractor semi-trailer, so as to improve the vehicle stability. Second a braking scheme and variable structure control with sliding mode control are introduced in order to achieve the best braking effect. Last Fishhook maneuver is introduced to the active safety simulation and the active control system effect verification. The simulation results show that multi-objective stability control algorithm of semi-trailer could improve the vehicle stability significantly during the transient maneuvers. The proposed multi-objective stability control algorithm is effective to prevent the vehicle rollover and jackknifing.
基金Project supported by the National Hi-Tech Research and Develop-ment Program (863) of China (No. 2006AA11Z204)the Qianji-ang Program of Zhejiang Province (No. 2009R10008)
文摘For automated vehicles,comfortable driving will improve passengers’ satisfaction.Reducing fuel consumption brings economic profits for car owners,decreases the impact on the environment and increases energy sustainability.In addition to comfort and fuel-economy,automated vehicles also have the basic requirements of safety and car-following.For this purpose,an adaptive cruise control (ACC) algorithm with multi-objectives is proposed based on a model predictive control (MPC) framework.In the proposed ACC algorithm,safety is guaranteed by constraining the inter-distance within a safe range; the requirements of comfort and car-following are considered to be the performance criteria and some optimal reference trajectories are introduced to increase fuel-economy.The performances of the proposed ACC algorithm are simulated and analyzed in five representative traffic scenarios and multiple experiments.The results show that not only are safety and car-following objectives satisfied,but also driving comfort and fuel-economy are improved significantly.
基金co-supported by the National Natural Science Foundation of China(No.11502008)Aeronautical Science Foundation of China(Nos.2017ZA51002,20185702003)the Fundamental Research Funds for the Central Universities of China(No.YWF-19-BJ-J-280)。
文摘For different flight phases in an overall flight mission,different control and allocation preferences should be pursued considering lift,drag or maneuverability characteristics.The multi-objective flight control allocation problem for a multi-phase flight mission is studied.For an overall flight mission,different flight phases namely climbing,cruise,maneuver and gliding phases are defined.Firstly,a multi-objective control allocation problem considering drag,lift or control energy preference is constructed.Secondly,considering different control preferences at different flight phases,the analytic hierarchical process method is used to construct a comprehensive performance index from different objectives such as lift or drag preferences.The active set based dynamic programming optimization method is used to solve the real-time optimization problem.For the validation,the Innovative Control Effector(ICE)tailless aircraft nonlinear model and the angular acceleration measurements based adaptive Incremental Backstepping(IBKS)are used to construct the validation platform.Finally,an overall flight mission is simulated to demonstrate the efficiency of the proposed multi-phase and multi-objective flight control allocation method.The results show that the comprehensive performance index for different phases,which are determined from the Analytic Hierarchy Process(AHP)method,can suitably satisfy the preference requirements for different flight phases.
