This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies.Hybrid composites,widely used in aerospace,automotive,and s...This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies.Hybrid composites,widely used in aerospace,automotive,and structural applications,often face variability in material properties,geometric configurations,and manufacturing processes,leading to uncertainty in their dynamic response.To address this,three surrogate-based machine learning approaches like radial basis function(RBF),multivariate adaptive regression splines(MARS),and polynomial neural networks(PNN)are integrated with a finite element framework to efficiently capture the stochastic behavior of these plates.The research focuses on predicting the first three natural frequencies under material uncertainties,which are critical to ensuring structural reliability.Monte Carlo simulation(MCS)is used as a benchmark for generating probabilistic datasets,including mean values,standard deviations,and probability density functions.The surrogate models are then trained and validated against these datasets,enabling accurate representation of uncertainty with substantially fewer samples compared to conventionalMCS.Among the methods studied,the RBFmodel demonstrates superior performance,closely approximating MCS results with a reduced sample size,thereby achieving significant computational savings.The proposed framework not only reduces computational time and costs but also maintains high predictive accuracy,making it well-suited for complex engineering systems.Beyond free vibration analysis,the methodology can be extended to more sophisticated scenarios,such as forced vibration,damping effects,and nonlinear structural responses.Overall,this work presents a computationally efficient and robust approach for surrogate-based uncertainty quantification,advancing the analysis and design of hybrid composite structures under uncertainty.展开更多
Federated Learning(FL)provides an effective framework for efficient processing in vehicular edge computing.However,the dynamic and uncertain communication environment,along with the performance variations of vehicular...Federated Learning(FL)provides an effective framework for efficient processing in vehicular edge computing.However,the dynamic and uncertain communication environment,along with the performance variations of vehicular devices,affect the distribution and uploading processes of model parameters.In FL-assisted Internet of Vehicles(IoV)scenarios,challenges such as data heterogeneity,limited device resources,and unstable communication environments become increasingly prominent.These issues necessitate intelligent vehicle selection schemes to enhance training efficiency.Given this context,we propose a new scenario involving FL-assisted IoV systems under dynamic and uncertain communication conditions,and develop a dynamic interval multi-objective optimization algorithm to jointly optimize various factors including training experiments,system energy consumption,and bandwidth utilization to meet multi-criteria resource optimization requirements.For the problem at hand,we design a dynamic interval multi-objective optimization algorithm based on interval overlap detection.Simulation results demonstrate that our method outperforms other solutions in terms of accuracy,training cost,and server utilization.It effectively enhances training efficiency under wireless channel environments while rationally utilizing bandwidth resources,thus possessing significant scientific value and application potential in the field of IoV.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
In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(...In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.展开更多
This paper introduces a sampled-data and intermittent-hold controller for nonlinear feedforward systems.The intermittent hold allows the control signal to be held in a portion of each sampled period,which does not req...This paper introduces a sampled-data and intermittent-hold controller for nonlinear feedforward systems.The intermittent hold allows the control signal to be held in a portion of each sampled period,which does not require the control to be persistently implemented,and thus has less control time.But,less control time degrades the performance of a continuous-time control system or even destabilizes it,especially when the holding portion is sufficiently small.To tackle this obstacle,we first introduce the notion of activating rate to describe the intermittent hold,and give the sampled-data and intermittent-hold controller based on some tuning parameters.Then it is proved that for any activating rate,these parameters can be designed to achieve the stability of the considered systems under appropriately choosing the sampling size.Finally,simulation examples are given to illustrate the effectiveness of the proposed method.展开更多
2025 has,so far,been a year of surprises.The United States under its reelected president,Donald Trump,has not only ramped up the U.S.trade war with China that he began in his first term,but has also unleashed a verbal...2025 has,so far,been a year of surprises.The United States under its reelected president,Donald Trump,has not only ramped up the U.S.trade war with China that he began in his first term,but has also unleashed a verbal assault and a barrage of tariffs against its neighbors,Canada and Mexico.It appears the age of globalization and rules-based free trade is over,at least as far as the giant U.S.economy is concerned.展开更多
The exact feedback linearization method implies an accurate knowledge of the model and its parameters.This assumption is an inherent limitation of the method,suffering from robustness issues.In general,the model struc...The exact feedback linearization method implies an accurate knowledge of the model and its parameters.This assumption is an inherent limitation of the method,suffering from robustness issues.In general,the model structure is only partially known and its parameters present uncertainties.The current paper extends the classical exact feedback linearization to the robust feedback linearization by adding an appropriatelydesigned robust control layer.This is then able to ensure robust stability and robust performance for the given uncertain system in a desired region of attraction.We consider the case of full relative degree input-affine nonlinear systems,which are of great practical importance in the literature.The inner loop contains the feedback linearization input for the nominal system and the resulting residual nonlinearities can always be characterized as inverse additive uncertainties.The constructive proofs provide exact representations of the uncertainty models in three considered scenarios:unmatched,fully-matched,and partially-matched uncertainties.The uncertainty model will be a descriptor system,which also represents one of the novelties of the paper.Our approach leads to a simplified control structure and a less conservative coverage of the uncertainty set compared to current alternatives.The end-to-end procedure is emphasized on an illustrative example,in two different hypotheses.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
This paper proposes an extension of the Modified-Plant ADRC(MP-ADRC)strategy to broaden its application to minimum phase dynamical systems.The main features of the MP-ADRC method are the inclusion of a constant gain i...This paper proposes an extension of the Modified-Plant ADRC(MP-ADRC)strategy to broaden its application to minimum phase dynamical systems.The main features of the MP-ADRC method are the inclusion of a constant gain in series with the plant output error and a linear filter in parallel with the overall error system.These structural changes do not influence the input/output dynamics of the original plant,but are intentionally introduced to modify the dynamics to be estimated by the extended state observer(ESO)and,thus,promote an increase in the robustness of the method.Some advantages can also be attributed to the proposed methodology,such as(i)the design procedures of both the controller and the ESO only require knowledge of the sign(±)of the plant input channel coefficient(or control gain);(ii)the plant control input is generated directly by a single ESO state variable.Despite the advantages and the characteristics of MP-ADRC mentioned earlier,closed-loop stability cannot be guaranteed when it is applied to dynamical systems that have finite zeros.To overcome this difficulty,this work introduces an extension in the MP-ADRC method.It basically consists of rewriting the minimum phase plant dynamics according to its relative order,and then follows with the design of the ESO by conveniently increasing the number of ESO state variables.The simulation results are also presented to illustrate the application of the proposed method.展开更多
Delay aware routing is now widely used to provide efficient network transmission. However, for newly developing or developed mobile communication networks(MCN), only limited delay data can be obtained. In such a netwo...Delay aware routing is now widely used to provide efficient network transmission. However, for newly developing or developed mobile communication networks(MCN), only limited delay data can be obtained. In such a network, the delay is with epistemic uncertainty, which makes the traditional routing scheme based on deterministic theory or probability theory not applicable. Motivated by this problem, the MCN with epistemic uncertainty is first summarized as a dynamic uncertain network based on uncertainty theory, which is widely applied to model epistemic uncertainties. Then by modeling the uncertain end-toend delay, a new delay bounded routing scheme is proposed to find the path with the maximum belief degree that satisfies the delay threshold for the dynamic uncertain network. Finally, a lowEarth-orbit satellite communication network(LEO-SCN) is used as a case to verify the effectiveness of our routing scheme. It is first modeled as a dynamic uncertain network, and then the delay bounded paths with the maximum belief degree are computed and compared under different delay thresholds.展开更多
To effectively select risk control schemes in uncertain environments,this paper has proposed an analysis and evaluation method based on the fuzzy comprehensive evaluation method.Firstly,enterprises have adopted the br...To effectively select risk control schemes in uncertain environments,this paper has proposed an analysis and evaluation method based on the fuzzy comprehensive evaluation method.Firstly,enterprises have adopted the brainstorming method and the Delphi method to identify risks in engineering projects,and organized the identified risks in the form of checklists to facilitate further analysis.Secondly,the fuzzy comprehensive evaluation theory was introduced to determine the comparison matrix of each risk factor and its weight.Furthermore,the top five risk factors in terms of weight ranking were taken as the evaluation factors for the selection of risk control plans.The plans were scored through the weighted scoring method,and the optimal risk control plan was determined based on the score.Finally,the feasibility of the proposed selection technology was verified through A research example of the risk control plan assessment for the construction project of Enterprise A.展开更多
With the increasing penetration of renewable energy resources in power systems,conventional timescale separated load frequency control(LFC)and economic dispatch may degrade frequency performance and reduce economic ef...With the increasing penetration of renewable energy resources in power systems,conventional timescale separated load frequency control(LFC)and economic dispatch may degrade frequency performance and reduce economic efficiency.This paper proposes a novel data-driven adaptive distributed optimal disturbance rejection control(DODRC)method for real-time economic LFC problem in nonlinear power systems.Firstly,a basic DODRC method is proposed by integrating the active disturbance rejection control method and the partial primal–dual algorithm.Then,to deal with the tie-line power flow constraints,the logarithmic barrier function is employed to reconstruct the Lagrange function to obtain the constrained DODRC method.By analyzing the sensitivity of the uncertain parameters of power systems,a data-driven adaptive DODRC method is finally proposed with a neural network.The effectiveness of the proposed method is demonstrated by experimental results using real-time equipment.展开更多
This paper delves into the problem of optimal placement conditions for a group of agents collaboratively localizing a target using range-only or bearing-only measurements.The challenge in this study stems from the unc...This paper delves into the problem of optimal placement conditions for a group of agents collaboratively localizing a target using range-only or bearing-only measurements.The challenge in this study stems from the uncertainty associated with the positions of the agents,which may experience drift or disturbances during the target localization process.Initially,we derive the Cramer-Rao lower bound(CRLB)of the target position as the primary analytical metric.Subsequently,we establish the necessary and sufficient conditions for the optimal placement of agents.Based on these conditions,we analyze the maximal allowable agent position error for an expected mean squared error(MSE),providing valuable guidance for the selection of agent positioning sensors.The analytical findings are further validated through simulation experiments.展开更多
As a crucial component of intelligent chassis systems,air suspension significantly enhances driver comfort and vehicle stability.To further improve the adaptability of commercial vehicles to complex and variable road ...As a crucial component of intelligent chassis systems,air suspension significantly enhances driver comfort and vehicle stability.To further improve the adaptability of commercial vehicles to complex and variable road conditions,this paper proposes a linear motor active suspension with quasi-zero stiffness(QZS)air spring system.Firstly,a dynamic model of the linear motor active suspension with QZS air spring system is established.Secondly,considering the random uncertainties in the linear motor parameters due to manufacturing and environmental factors,a dynamic model and state equations incorporating these uncertainties are constructed using the polynomial chaos expansion(PCE)method.Then,based on H_(2) robust control theory and the Kalman filter,a state feedback control law is derived,accounting for the random parameter uncertainties.Finally,simulation and hardware-in-the-loop(HIL)experimental results demonstrate that the PCE-H_(2) robust controller not only provides better performance in terms of vehicle ride comfort compared to general H_(2) robust controller but also exhibits higher robustness to the effects of random uncertain parameters,resulting in more stable control performance.展开更多
Due to abrupt changes in the intrinsic degradation mechanism or shock from external environmental pressure,degradations of some equipment are characterized by multi-phase and jumps.Meanwhile,equipment is subject to in...Due to abrupt changes in the intrinsic degradation mechanism or shock from external environmental pressure,degradations of some equipment are characterized by multi-phase and jumps.Meanwhile,equipment is subject to inherent fluctuations,limited data and imperfect measurements resulting in aleatory,epistemic and measurement uncertainties of the degradation process.This paper proposes a degradation model and remaining useful life(RUL)prediction method under triple uncertainties for a category of complex equipment with multi-phase degradation and jumps.First,a multi-phase degradation model with random jumps and measurement errors is constructed based on uncertain random processes.Afterward,the analytic expression of RUL prediction considering the heterogeneity is derived by modeling the uncertainty of degradation states at change points under the concept of first hitting time.A stochastic uncertain approach is utilized for the proposed multi-phase degradation model to identify model parameters based on historical data.Furthermore,the implied degradation features are adaptively updated in online stage using similarity-based weighted stochastic uncertain maximum likelihood estimation and Kalman filtering.Finally,the effectiveness of the method is verified by simulation example and practical case.展开更多
In order to guarantee the safety service and life-span of long-span cable-stayed bridges, the uncertain type of analytic hierarchy process （AHP） method is adopted to access the bridge condition. The correlative theo...In order to guarantee the safety service and life-span of long-span cable-stayed bridges, the uncertain type of analytic hierarchy process （AHP） method is adopted to access the bridge condition. The correlative theory and applied objects of uncertain type of AHP are introduced, and then the optimal transitive matrix method is chosen to calculate the interval number judgment matrix, which makes the weights of indices more reliable and accurate. Finally, with Harbin Songhua River Cable-Stayed Bridge as an example, an index system and an assessment model are proposed for the condition assessment of this bridge, and by using uncertain type of AHP, the weights of assessment indices are fixed and the final assessment results of the bridge are calculated, which proves the feasibility and practicability of this method. The application of this assessment method can provide the scientific basis for maintenance and management of long-span cable-stayed bridges.展开更多
To address the problem that a general augmented state Kalman filter or a two-stage Kalman filter cannot achieve satisfactory positioning performance when facing uncertain noise of the micro-electro-mechanical system（...To address the problem that a general augmented state Kalman filter or a two-stage Kalman filter cannot achieve satisfactory positioning performance when facing uncertain noise of the micro-electro-mechanical system（MEMS） inertial sensors, a novel interacting multiple model-based two-stage Kalman filter（IMM-TSKF） is proposed to adapt to the uncertain inertial sensor noise. Three bias filters are developed based on different noise characteristics to cover a wide range of noise levels. Then, an accurate estimation of biases is calculated by the interacting multiple model algorithm to correct the bias-free filter. Thus, the vehicle positioning system can achieve good performance when suffering from uncertain inertial sensor noise. The experimental results indicate that the average position error of the proposed IMMTSKF is 25% lower than that of the general TSKF.展开更多
A transonic airfoil designed by means of classical point-optimization may result in its dramatically inferior performance under off-design conditions. To overcome this shortcoming, robust design is proposed to find ou...A transonic airfoil designed by means of classical point-optimization may result in its dramatically inferior performance under off-design conditions. To overcome this shortcoming, robust design is proposed to find out the optimal profile of an airfoil to maintain its performance in an uncertain environment. The robust airfoil optimization is aimed to minimize mean values and variances of drag coefficients while satisfying the lift and thickness constraints over a range of Mach numbers. A multi-objective estimation of distribution algorithm is applied to the robust airfoil optimization on the base of the RAE2822 benchmark airfoil. The shape of the airfoil is obtained through superposing ten Hick-Henne shape functions upon the benchmark airfoil. A set of design points is selected according to a uniform design table for aerodynamic evaluation. A Kriging model of drag coefficient is constructed with those points to reduce computing costs. Over the Mach range from 0.7 to 0.8, the airfoil generated by the robust optimization has a configuration characterized by supercritical airfoil with low drag coefficients. The small fluctuation in its drag coefficients means that the performance of the robust airfoil is insensitive to variation of Mach number.展开更多
To avoid the aerodynamic performance loss of airfoil at non-design state which often appears in single point design optimization, and to improve the adaptability to the uncertain factors in actual flight environment, ...To avoid the aerodynamic performance loss of airfoil at non-design state which often appears in single point design optimization, and to improve the adaptability to the uncertain factors in actual flight environment, a two-dimensional stochastic airfoil optimization design method based on neural networks is presented. To provide highly efficient and credible analysis, four BP neural networks are built as surrogate models to predict the airfoil aerodynamic coefficients and geometry parameter. These networks are combined with the probability density function obeying normal distribution and the genetic algorithm, thus forming an optimization design method. Using the method, for GA（W）-2 airfoil, a stochastic optimization is implemented in a two-dimensional flight area about Mach number and angle of attack. Compared with original airfoil and single point optimization design airfoil, results show that the two-dimensional stochastic method can improve the performance in a specific flight area, and increase the airfoil adaptability to the stochastic changes of multiple flight parameters.展开更多
文摘This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies.Hybrid composites,widely used in aerospace,automotive,and structural applications,often face variability in material properties,geometric configurations,and manufacturing processes,leading to uncertainty in their dynamic response.To address this,three surrogate-based machine learning approaches like radial basis function(RBF),multivariate adaptive regression splines(MARS),and polynomial neural networks(PNN)are integrated with a finite element framework to efficiently capture the stochastic behavior of these plates.The research focuses on predicting the first three natural frequencies under material uncertainties,which are critical to ensuring structural reliability.Monte Carlo simulation(MCS)is used as a benchmark for generating probabilistic datasets,including mean values,standard deviations,and probability density functions.The surrogate models are then trained and validated against these datasets,enabling accurate representation of uncertainty with substantially fewer samples compared to conventionalMCS.Among the methods studied,the RBFmodel demonstrates superior performance,closely approximating MCS results with a reduced sample size,thereby achieving significant computational savings.The proposed framework not only reduces computational time and costs but also maintains high predictive accuracy,making it well-suited for complex engineering systems.Beyond free vibration analysis,the methodology can be extended to more sophisticated scenarios,such as forced vibration,damping effects,and nonlinear structural responses.Overall,this work presents a computationally efficient and robust approach for surrogate-based uncertainty quantification,advancing the analysis and design of hybrid composite structures under uncertainty.
基金supported in part by the Central Guidance for Local Science and Technology Development Funds under Grant No.YDZJSX2025D049Shanxi Provincial Graduate Innovation Research Program under Grant No.2024KY652.
文摘Federated Learning(FL)provides an effective framework for efficient processing in vehicular edge computing.However,the dynamic and uncertain communication environment,along with the performance variations of vehicular devices,affect the distribution and uploading processes of model parameters.In FL-assisted Internet of Vehicles(IoV)scenarios,challenges such as data heterogeneity,limited device resources,and unstable communication environments become increasingly prominent.These issues necessitate intelligent vehicle selection schemes to enhance training efficiency.Given this context,we propose a new scenario involving FL-assisted IoV systems under dynamic and uncertain communication conditions,and develop a dynamic interval multi-objective optimization algorithm to jointly optimize various factors including training experiments,system energy consumption,and bandwidth utilization to meet multi-criteria resource optimization requirements.For the problem at hand,we design a dynamic interval multi-objective optimization algorithm based on interval overlap detection.Simulation results demonstrate that our method outperforms other solutions in terms of accuracy,training cost,and server utilization.It effectively enhances training efficiency under wireless channel environments while rationally utilizing bandwidth resources,thus possessing significant scientific value and application potential in the field of IoV.
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 by the CAS Project for Young Scientists in Basic Research under Grant YSBR-035Jiangsu Provincial Key Research and Development Program under Grant BE2021013-2.
文摘In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.
基金supported in part by research grants from the National Natural Science Foundation of China(12201365)the Natural Science Foundation of Shandong Province(ZR2021QF106).
文摘This paper introduces a sampled-data and intermittent-hold controller for nonlinear feedforward systems.The intermittent hold allows the control signal to be held in a portion of each sampled period,which does not require the control to be persistently implemented,and thus has less control time.But,less control time degrades the performance of a continuous-time control system or even destabilizes it,especially when the holding portion is sufficiently small.To tackle this obstacle,we first introduce the notion of activating rate to describe the intermittent hold,and give the sampled-data and intermittent-hold controller based on some tuning parameters.Then it is proved that for any activating rate,these parameters can be designed to achieve the stability of the considered systems under appropriately choosing the sampling size.Finally,simulation examples are given to illustrate the effectiveness of the proposed method.
文摘2025 has,so far,been a year of surprises.The United States under its reelected president,Donald Trump,has not only ramped up the U.S.trade war with China that he began in his first term,but has also unleashed a verbal assault and a barrage of tariffs against its neighbors,Canada and Mexico.It appears the age of globalization and rules-based free trade is over,at least as far as the giant U.S.economy is concerned.
基金funded by the project new smart and adaptive robotics solutions for personalized minimally invasive surgery in cancer treatment−ATHENA,European Union-NextGenerationEU and Romanian Government,under National Recovery and Resilience Plan for Romania(CF116/15.11.2022)through the Romanian Ministry of Research,Innovation and Digitalization(within Component 9,investment I8)。
文摘The exact feedback linearization method implies an accurate knowledge of the model and its parameters.This assumption is an inherent limitation of the method,suffering from robustness issues.In general,the model structure is only partially known and its parameters present uncertainties.The current paper extends the classical exact feedback linearization to the robust feedback linearization by adding an appropriatelydesigned robust control layer.This is then able to ensure robust stability and robust performance for the given uncertain system in a desired region of attraction.We consider the case of full relative degree input-affine nonlinear systems,which are of great practical importance in the literature.The inner loop contains the feedback linearization input for the nominal system and the resulting residual nonlinearities can always be characterized as inverse additive uncertainties.The constructive proofs provide exact representations of the uncertainty models in three considered scenarios:unmatched,fully-matched,and partially-matched uncertainties.The uncertainty model will be a descriptor system,which also represents one of the novelties of the paper.Our approach leads to a simplified control structure and a less conservative coverage of the uncertainty set compared to current alternatives.The end-to-end procedure is emphasized on an illustrative example,in two different hypotheses.
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 in part by the Brazilian research agencies CNPq and CAPESby the Fundação Carlos Chagas Filho de AmparoàPesquisa do Estado do Rio de Janeiro,FAPERJ-Brasil(Project E-26/210.425/2024).
文摘This paper proposes an extension of the Modified-Plant ADRC(MP-ADRC)strategy to broaden its application to minimum phase dynamical systems.The main features of the MP-ADRC method are the inclusion of a constant gain in series with the plant output error and a linear filter in parallel with the overall error system.These structural changes do not influence the input/output dynamics of the original plant,but are intentionally introduced to modify the dynamics to be estimated by the extended state observer(ESO)and,thus,promote an increase in the robustness of the method.Some advantages can also be attributed to the proposed methodology,such as(i)the design procedures of both the controller and the ESO only require knowledge of the sign(±)of the plant input channel coefficient(or control gain);(ii)the plant control input is generated directly by a single ESO state variable.Despite the advantages and the characteristics of MP-ADRC mentioned earlier,closed-loop stability cannot be guaranteed when it is applied to dynamical systems that have finite zeros.To overcome this difficulty,this work introduces an extension in the MP-ADRC method.It basically consists of rewriting the minimum phase plant dynamics according to its relative order,and then follows with the design of the ESO by conveniently increasing the number of ESO state variables.The simulation results are also presented to illustrate the application of the proposed method.
基金National Natural Science Foundation of China (61773044,62073009)National key Laboratory of Science and Technology on Reliability and Environmental Engineering(WDZC2019601A301)。
文摘Delay aware routing is now widely used to provide efficient network transmission. However, for newly developing or developed mobile communication networks(MCN), only limited delay data can be obtained. In such a network, the delay is with epistemic uncertainty, which makes the traditional routing scheme based on deterministic theory or probability theory not applicable. Motivated by this problem, the MCN with epistemic uncertainty is first summarized as a dynamic uncertain network based on uncertainty theory, which is widely applied to model epistemic uncertainties. Then by modeling the uncertain end-toend delay, a new delay bounded routing scheme is proposed to find the path with the maximum belief degree that satisfies the delay threshold for the dynamic uncertain network. Finally, a lowEarth-orbit satellite communication network(LEO-SCN) is used as a case to verify the effectiveness of our routing scheme. It is first modeled as a dynamic uncertain network, and then the delay bounded paths with the maximum belief degree are computed and compared under different delay thresholds.
文摘To effectively select risk control schemes in uncertain environments,this paper has proposed an analysis and evaluation method based on the fuzzy comprehensive evaluation method.Firstly,enterprises have adopted the brainstorming method and the Delphi method to identify risks in engineering projects,and organized the identified risks in the form of checklists to facilitate further analysis.Secondly,the fuzzy comprehensive evaluation theory was introduced to determine the comparison matrix of each risk factor and its weight.Furthermore,the top five risk factors in terms of weight ranking were taken as the evaluation factors for the selection of risk control plans.The plans were scored through the weighted scoring method,and the optimal risk control plan was determined based on the score.Finally,the feasibility of the proposed selection technology was verified through A research example of the risk control plan assessment for the construction project of Enterprise A.
基金supported in part by the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources under Grant LAPS24009in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515110016in part by the National Natural Science Foundation of China under Grant 52206009.
文摘With the increasing penetration of renewable energy resources in power systems,conventional timescale separated load frequency control(LFC)and economic dispatch may degrade frequency performance and reduce economic efficiency.This paper proposes a novel data-driven adaptive distributed optimal disturbance rejection control(DODRC)method for real-time economic LFC problem in nonlinear power systems.Firstly,a basic DODRC method is proposed by integrating the active disturbance rejection control method and the partial primal–dual algorithm.Then,to deal with the tie-line power flow constraints,the logarithmic barrier function is employed to reconstruct the Lagrange function to obtain the constrained DODRC method.By analyzing the sensitivity of the uncertain parameters of power systems,a data-driven adaptive DODRC method is finally proposed with a neural network.The effectiveness of the proposed method is demonstrated by experimental results using real-time equipment.
文摘This paper delves into the problem of optimal placement conditions for a group of agents collaboratively localizing a target using range-only or bearing-only measurements.The challenge in this study stems from the uncertainty associated with the positions of the agents,which may experience drift or disturbances during the target localization process.Initially,we derive the Cramer-Rao lower bound(CRLB)of the target position as the primary analytical metric.Subsequently,we establish the necessary and sufficient conditions for the optimal placement of agents.Based on these conditions,we analyze the maximal allowable agent position error for an expected mean squared error(MSE),providing valuable guidance for the selection of agent positioning sensors.The analytical findings are further validated through simulation experiments.
基金Supported by National Natural Science Foundation of China(Grant No.51875256)Open Platform Fund of Human Institute of Technology(Grant No.KFA22009).
文摘As a crucial component of intelligent chassis systems,air suspension significantly enhances driver comfort and vehicle stability.To further improve the adaptability of commercial vehicles to complex and variable road conditions,this paper proposes a linear motor active suspension with quasi-zero stiffness(QZS)air spring system.Firstly,a dynamic model of the linear motor active suspension with QZS air spring system is established.Secondly,considering the random uncertainties in the linear motor parameters due to manufacturing and environmental factors,a dynamic model and state equations incorporating these uncertainties are constructed using the polynomial chaos expansion(PCE)method.Then,based on H_(2) robust control theory and the Kalman filter,a state feedback control law is derived,accounting for the random parameter uncertainties.Finally,simulation and hardware-in-the-loop(HIL)experimental results demonstrate that the PCE-H_(2) robust controller not only provides better performance in terms of vehicle ride comfort compared to general H_(2) robust controller but also exhibits higher robustness to the effects of random uncertain parameters,resulting in more stable control performance.
基金supported by the National Key Research and Development Program of China(2021YFB3301200)the National Natural Science Foundation of China(NSFC)(U21A20483,62373040,62203042).
文摘Due to abrupt changes in the intrinsic degradation mechanism or shock from external environmental pressure,degradations of some equipment are characterized by multi-phase and jumps.Meanwhile,equipment is subject to inherent fluctuations,limited data and imperfect measurements resulting in aleatory,epistemic and measurement uncertainties of the degradation process.This paper proposes a degradation model and remaining useful life(RUL)prediction method under triple uncertainties for a category of complex equipment with multi-phase degradation and jumps.First,a multi-phase degradation model with random jumps and measurement errors is constructed based on uncertain random processes.Afterward,the analytic expression of RUL prediction considering the heterogeneity is derived by modeling the uncertainty of degradation states at change points under the concept of first hitting time.A stochastic uncertain approach is utilized for the proposed multi-phase degradation model to identify model parameters based on historical data.Furthermore,the implied degradation features are adaptively updated in online stage using similarity-based weighted stochastic uncertain maximum likelihood estimation and Kalman filtering.Finally,the effectiveness of the method is verified by simulation example and practical case.
基金Specialized Research Fund for the Doctoral Programof Higher Education (No20050213008)the Scientific and TechnicalPlan Item of Communications Department of Heilongjiang Province ofChina (2004)
文摘In order to guarantee the safety service and life-span of long-span cable-stayed bridges, the uncertain type of analytic hierarchy process （AHP） method is adopted to access the bridge condition. The correlative theory and applied objects of uncertain type of AHP are introduced, and then the optimal transitive matrix method is chosen to calculate the interval number judgment matrix, which makes the weights of indices more reliable and accurate. Finally, with Harbin Songhua River Cable-Stayed Bridge as an example, an index system and an assessment model are proposed for the condition assessment of this bridge, and by using uncertain type of AHP, the weights of assessment indices are fixed and the final assessment results of the bridge are calculated, which proves the feasibility and practicability of this method. The application of this assessment method can provide the scientific basis for maintenance and management of long-span cable-stayed bridges.
基金The National Natural Science Foundation of China(No.61273236)the Scientific Research Foundation of Graduate School of Southeast University(No.YBJJ1637),China Scholarship Council
文摘To address the problem that a general augmented state Kalman filter or a two-stage Kalman filter cannot achieve satisfactory positioning performance when facing uncertain noise of the micro-electro-mechanical system（MEMS） inertial sensors, a novel interacting multiple model-based two-stage Kalman filter（IMM-TSKF） is proposed to adapt to the uncertain inertial sensor noise. Three bias filters are developed based on different noise characteristics to cover a wide range of noise levels. Then, an accurate estimation of biases is calculated by the interacting multiple model algorithm to correct the bias-free filter. Thus, the vehicle positioning system can achieve good performance when suffering from uncertain inertial sensor noise. The experimental results indicate that the average position error of the proposed IMMTSKF is 25% lower than that of the general TSKF.
基金National Natural Science Foundation of China (10377015)
文摘A transonic airfoil designed by means of classical point-optimization may result in its dramatically inferior performance under off-design conditions. To overcome this shortcoming, robust design is proposed to find out the optimal profile of an airfoil to maintain its performance in an uncertain environment. The robust airfoil optimization is aimed to minimize mean values and variances of drag coefficients while satisfying the lift and thickness constraints over a range of Mach numbers. A multi-objective estimation of distribution algorithm is applied to the robust airfoil optimization on the base of the RAE2822 benchmark airfoil. The shape of the airfoil is obtained through superposing ten Hick-Henne shape functions upon the benchmark airfoil. A set of design points is selected according to a uniform design table for aerodynamic evaluation. A Kriging model of drag coefficient is constructed with those points to reduce computing costs. Over the Mach range from 0.7 to 0.8, the airfoil generated by the robust optimization has a configuration characterized by supercritical airfoil with low drag coefficients. The small fluctuation in its drag coefficients means that the performance of the robust airfoil is insensitive to variation of Mach number.
文摘To avoid the aerodynamic performance loss of airfoil at non-design state which often appears in single point design optimization, and to improve the adaptability to the uncertain factors in actual flight environment, a two-dimensional stochastic airfoil optimization design method based on neural networks is presented. To provide highly efficient and credible analysis, four BP neural networks are built as surrogate models to predict the airfoil aerodynamic coefficients and geometry parameter. These networks are combined with the probability density function obeying normal distribution and the genetic algorithm, thus forming an optimization design method. Using the method, for GA（W）-2 airfoil, a stochastic optimization is implemented in a two-dimensional flight area about Mach number and angle of attack. Compared with original airfoil and single point optimization design airfoil, results show that the two-dimensional stochastic method can improve the performance in a specific flight area, and increase the airfoil adaptability to the stochastic changes of multiple flight parameters.
