Risk management often plays an important role in decision making un-der uncertainty.In quantitative risk management,assessing and optimizing risk metrics requires eficient computing techniques and reliable theoretical...Risk management often plays an important role in decision making un-der uncertainty.In quantitative risk management,assessing and optimizing risk metrics requires eficient computing techniques and reliable theoretical guarantees.In this pa-per,we introduce several topics on quantitative risk management and review some of the recent studies and advancements on the topics.We consider several risk metrics and study decision models that involve the metrics,with a main focus on the related com-puting techniques and theoretical properties.We show that stochastic optimization,as a powerful tool,can be leveraged to effectively address these problems.展开更多
The article studies the evolutionary dynamics of two-population two-strategy game models with and without impulses. First, the payment matrix is given and two evolutionary dynamics models are established by adding sto...The article studies the evolutionary dynamics of two-population two-strategy game models with and without impulses. First, the payment matrix is given and two evolutionary dynamics models are established by adding stochastic and impulse. For the stochastic model without impulses, the existence and uniqueness of solution, and the existence of positive periodic solutions are proved, and a sufficient condition for strategy extinction is given. For the stochastic model with impulses, the existence of positive periodic solutions is proved. Numerical results show that noise and impulses directly affect the model, but the periodicity of the model does not change.展开更多
In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained f...In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.展开更多
This paper studies the Smoluchowski–Kramers approximation for a discrete-time dynamical system modeled as the motion of a particle in a force field.We show that the approximation holds for the drift-implicit Euler–M...This paper studies the Smoluchowski–Kramers approximation for a discrete-time dynamical system modeled as the motion of a particle in a force field.We show that the approximation holds for the drift-implicit Euler–Maruyama discretization and derive its convergence rate.In particular,the solution of the discretized system converges to the solution of the first-order limit equation in the mean-square sense,and this convergence is independent of the order in which the mass parameterμand the step size h tend to zero.展开更多
This paper investigates the selective maintenance o systems that perform multi-mission in succession. Selective maintenance is performed on systems with limited break time to improve the success of the next mission. I...This paper investigates the selective maintenance o systems that perform multi-mission in succession. Selective maintenance is performed on systems with limited break time to improve the success of the next mission. In general, the duration of the mission is stochastic. However, existing studies rarely take into account system availability and the repairpersons with different skill levels. To solve this problem, a new multi-mission selective maintenance and repairpersons assignment model with stochastic duration of the mission are developed. To maximize the minimum phase-mission reliability while meeting the minimum system availability, the model is transformed into an optimization problem subject to limited maintenance resources. The optimization is then realized using an analytical method based on a self-programming function and a Monte Carlo simulation method, respectively. Finally, the validity of the model and solution method approaches are verified by numerical arithmetic examples. Comparative and sensitivity analyses are made to provide proven recommendations for decision-makers.展开更多
This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with...This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with one source element and multiple sink elements is considered first.Each element can con-nect with other elements within a stochastic connection ranges.The system is regarded as successful as long as the source ele-ment remains connected with all sink elements.An importance measure is proposed to evaluate the performance of non-source elements.Furthermore,to calculate the system reliability and the element importance measure,a multi-valued decision diagram based approach is structured and its complexity is analyzed.Finally,a numerical example about the signal transfer station system is illustrated to analyze the system reliability and the ele-ment importance measure.展开更多
We study the distribution limit of a class of stochastic evolution equation driven by an additive-stable Non-Gaussian process in the case of α∈(1,2).We prove that,under suitable conditions,the law of the solution co...We study the distribution limit of a class of stochastic evolution equation driven by an additive-stable Non-Gaussian process in the case of α∈(1,2).We prove that,under suitable conditions,the law of the solution converges weakly to the law of a stochastic evolution equation with an additive Gaussian process.展开更多
The problem of robust H_∞ control for uncertain neutral stochastic systems with time-varying delay is discussed.The parameter uncertaintie is assumed to be time varying norm-bounded.First,the stochastic robust stabil...The problem of robust H_∞ control for uncertain neutral stochastic systems with time-varying delay is discussed.The parameter uncertaintie is assumed to be time varying norm-bounded.First,the stochastic robust stabilization of the stochastic system without disturbance input is investigated by nonlinear matrix inequality method.Then,a full-order stochastic dynamic output feedback controller is designed by solving a bilinear matrix inequality（BMI）,which ensures a prescribed stochastic robust H_∞ performance level for the resulting closed-loop system with nonzero disturbance input and for all admissible uncertainties.An illustrative example is provided to show the feasibility of the controller and the potential of the proposed technique.展开更多
A consensus-distributed fault-tolerant(CDFT)control law is proposed for a class of leader-following multi-vehicle cooperative attack(MVCA)systems in this paper.In particular,the switching communication topologies,stoc...A consensus-distributed fault-tolerant(CDFT)control law is proposed for a class of leader-following multi-vehicle cooperative attack(MVCA)systems in this paper.In particular,the switching communication topologies,stochastic multi-hop timevarying delays,and actuator faults are considered,which may lead to system performance degradation or on certain occasions even cause system instability.Firstly,the estimator of actuator faults for the following vehicle is designed to identify the actuator faults under a fixed topology.Then the CDFT control protocol and trajectory following error are derived by the relevant content of Lyapunov stability theory,the graph theory,and the matrix theory.The CDFT control protocol is proposed in the same manner,where a more realistic scenario is considered,in which the maximum trajectory following error and information on the switching topologies during the cooperative attack are available.Finally,numerical simulation are carried out to indicate that the proposed distributed fault-tolerant(DFT)control law is effective.展开更多
Stochastic switched epidemic systems with a discrete or distributed time delay are constructed and investigated. By the Lyapunov method and lto＇s differential rule, the existence and uniqueness of global positive sol...Stochastic switched epidemic systems with a discrete or distributed time delay are constructed and investigated. By the Lyapunov method and lto＇s differential rule, the existence and uniqueness of global positive solution of each system is proved. And stability conditions of the disease-free equilibrium of the systems are obtained. Numerical simulations are presented to illustrate the results.展开更多
A robust dissipative control problem for a class of It-type stochastic systems is discussed with Markovian jumping parameters and time-varying delay. A memoryless state feedback dissipative controller is developed bas...A robust dissipative control problem for a class of It-type stochastic systems is discussed with Markovian jumping parameters and time-varying delay. A memoryless state feedback dissipative controller is developed based on Lyapunov-Krasovskii functional approach such that the closed-loop system is robustly stochastically stable and weakly delay-dependent （RSSWDD） and strictly （Q, S, R）-dissipative. The sufficient condition on the existence of state feedback dissipative controller is presented by linear matrix inequality （LMI）. And the desired controller can be concluded as solving a set of LMI. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed approach.展开更多
A new horn failure mechanism was constructed for tunnel faces in the soft rock mass by means of the logarithmic spiral curve. The seismic action was incorporated into the horn failure mechanism using the pseudo-static...A new horn failure mechanism was constructed for tunnel faces in the soft rock mass by means of the logarithmic spiral curve. The seismic action was incorporated into the horn failure mechanism using the pseudo-static method. Considering the randomness of rock mass parameters and loads, a three-dimensional (3D) stochastic collapse model was established. Reliability analysis of seismic stability of tunnel faces was presented via the kinematical approach and the response surface method. The results show that, the reliability of tunnel faces is significantly affected by the supporting pressure, geological strength index, uniaxial compressive strength, rock bulk density and seismic forces. It is worth noting that, if the effect of seismic force was not considered, the stability of tunnel faces would be obviously overestimated. However, the correlation between horizontal and vertical seismic forces can be ignored under the condition of low calculation accuracy.展开更多
Developing a course of action(COA) is a key step in military planning. In most extant studies on the COA development,only the unilateral actions of friendly forces are considered. Based on stochastic games, we propose...Developing a course of action(COA) is a key step in military planning. In most extant studies on the COA development,only the unilateral actions of friendly forces are considered. Based on stochastic games, we propose models that could deal with the complexities and uncertainties of wars. By analyzing the equilibrium state of both opponent sides, outcomes preferable to one side could be achieved by adopting the methods obtained from the proposed models. This research could help decision makers take the right COA in a state of uncertainty.展开更多
As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configu...As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configuration network(SCN)with robust technique,namely,robust SCN(RSCN).Firstly,this paper proves the universal approximation property of RSCN with weighted least squares technique.Secondly,three robust algorithms are presented by employing M-estimation with Huber loss function,M-estimation with interquartile range(IQR)and nonparametric kernel density estimation(NKDE)function respectively to set the penalty weight.Comparison experiments are first carried out based on the UCI standard data sets to verify the effectiveness of these methods,and then the data-driven PS model based on the robust algorithms are established and verified.Experimental results show that the RSCN has an excellent performance for the PS estimation.展开更多
Multi-attribute decision problems where the performances of the alternatives are random variables are considered. The suggested approach grades the probabilities of preference of one alternative over another with resp...Multi-attribute decision problems where the performances of the alternatives are random variables are considered. The suggested approach grades the probabilities of preference of one alternative over another with respect to the same attribute. Based on the graded probabilistic dominance relation, the pairwise comparison information table is defined. The global preferences of the decision maker can be seen as a rough binary relation. The present paper proposes to approximate this preference relation by means of the graded probabilistic dominance relation with respect to the subsets of attributes. At last, the method is illustrated by an example.展开更多
The traditional voltage stability analysis method is mostly based on the deterministic mode1.and ignores the uncertainties of bus loads,power supplies,changes in network configuration and so on.However,the great expan...The traditional voltage stability analysis method is mostly based on the deterministic mode1.and ignores the uncertainties of bus loads,power supplies,changes in network configuration and so on.However,the great expansion of renewable power generations such as wind and solar energy in a power system has increased their uncertainty,and仃aditional techniques are limited in capturing their variable behavior.This leads to greater needs of new techniques and methodologies to properly quan tify the voltage stability of power systems.展开更多
A new fault tolerant control（FTC） via a controller reconfiguration approach for general stochastic nonlinear systems is studied.Different from the formulation of classical FTC methods,it is supposed that the measure...A new fault tolerant control（FTC） via a controller reconfiguration approach for general stochastic nonlinear systems is studied.Different from the formulation of classical FTC methods,it is supposed that the measured information for the FTC is the probability density functions（PDFs） of the system output rather than its measured value.A radial basis functions（RBFs） neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network.As a result,the nonlinear FTC problem subject to dynamic relation between the input and the output PDFs can be transformed into a nonlinear FTC problem subject to dynamic relation between the control input and the weights of the RBFs neural network approximation to the output PDFs.The FTC design consists of two steps.The first step is fault detection and diagnosis（FDD）,which can produce an alarm when there is a fault in the system and also locate which component has a fault.The second step is to adapt the controller to the faulty case so that the system is able to achieve its target.A linear matrix inequality（LMI） based feasible FTC method is applied such that the fault can be detected and diagnosed.An illustrated example is included to demonstrate the efficiency of the proposed algorithm,and satisfactory results have been obtained.展开更多
When the bi-stable stochastic resonance method was applied to enhance weak thruster fault for autonomous underwater vehicle(AUV), the enhancement performance could not satisfy the detection requirement of weak thruste...When the bi-stable stochastic resonance method was applied to enhance weak thruster fault for autonomous underwater vehicle(AUV), the enhancement performance could not satisfy the detection requirement of weak thruster fault. As for this problem, a fault feature enhancement method based on mono-stable stochastic resonance was proposed. In the method, in order to improve the enhancement performance of weak thruster fault feature, the conventional bi-stable potential function was changed to mono-stable potential function which was more suitable for aperiodic signals. Furthermore, when particle swarm optimization was adopted to adjust the parameters of mono-stable stochastic resonance system, the global convergent time would be long. An improved particle swarm optimization method was developed by changing the linear inertial weighted function as nonlinear function with cosine function, so as to reduce the global convergent time. In addition, when the conventional wavelet reconstruction method was adopted to detect the weak thruster fault, undetected fault or false alarm may occur. In order to successfully detect the weak thruster fault, a weak thruster detection method was proposed based on the integration of stochastic resonance and wavelet reconstruction. In the method, the optimal reconstruction scale was determined by comparing wavelet entropies corresponding to each decomposition scale. Finally, pool-experiments were performed on AUV with thruster fault. The effectiveness of the proposed mono-stable stochastic resonance method in enhancing fault feature and reducing the global convergent time was demonstrated in comparison with particle swarm optimization based bi-stochastic resonance method. Furthermore, the effectiveness of the proposed fault detection method was illustrated in comparison with the conventional wavelet reconstruction.展开更多
The stochastic convergence of the cubature Kalmanfilter with intermittent observations （CKFI） for general nonlinearstochastic systems is investigated. The Bernoulli distributed ran-dom variable is employed to descri...The stochastic convergence of the cubature Kalmanfilter with intermittent observations （CKFI） for general nonlinearstochastic systems is investigated. The Bernoulli distributed ran-dom variable is employed to describe the phenomenon of intermit-tent observations. According to the cubature sample principle, theestimation error and the error covariance matrix （ECM） of CKFIare derived by Taylor series expansion, respectively. Afterwards, itis theoretically proved that the ECM will be bounded if the obser-vation arrival probability exceeds a critical minimum observationarrival probability. Meanwhile, under proper assumption corre-sponding with real engineering situations, the stochastic stabilityof the estimation error can be guaranteed when the initial estima-tion error and the stochastic noise terms are sufficiently small. Thetheoretical conclusions are verified by numerical simulations fortwo illustrative examples; also by evaluating the tracking perfor-mance of the optical-electric target tracking system implementedby CKFI and unscented Kalman filter with intermittent observa-tions （UKFI） separately, it is demonstrated that the proposed CKFIslightly outperforms the UKFI with respect to tracking accuracy aswell as real time performance.展开更多
文摘Risk management often plays an important role in decision making un-der uncertainty.In quantitative risk management,assessing and optimizing risk metrics requires eficient computing techniques and reliable theoretical guarantees.In this pa-per,we introduce several topics on quantitative risk management and review some of the recent studies and advancements on the topics.We consider several risk metrics and study decision models that involve the metrics,with a main focus on the related com-puting techniques and theoretical properties.We show that stochastic optimization,as a powerful tool,can be leveraged to effectively address these problems.
基金Supported by the National Natural Science Foundation of China(10671182)。
文摘The article studies the evolutionary dynamics of two-population two-strategy game models with and without impulses. First, the payment matrix is given and two evolutionary dynamics models are established by adding stochastic and impulse. For the stochastic model without impulses, the existence and uniqueness of solution, and the existence of positive periodic solutions are proved, and a sufficient condition for strategy extinction is given. For the stochastic model with impulses, the existence of positive periodic solutions is proved. Numerical results show that noise and impulses directly affect the model, but the periodicity of the model does not change.
基金Supported by the National Natural Science Foundation of China(11971458,11471310)。
文摘In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.
基金supported by the PhD Research Startup Foundation of Hubei University of Economics(Grand No.XJ23BS42).
文摘This paper studies the Smoluchowski–Kramers approximation for a discrete-time dynamical system modeled as the motion of a particle in a force field.We show that the approximation holds for the drift-implicit Euler–Maruyama discretization and derive its convergence rate.In particular,the solution of the discretized system converges to the solution of the first-order limit equation in the mean-square sense,and this convergence is independent of the order in which the mass parameterμand the step size h tend to zero.
文摘This paper investigates the selective maintenance o systems that perform multi-mission in succession. Selective maintenance is performed on systems with limited break time to improve the success of the next mission. In general, the duration of the mission is stochastic. However, existing studies rarely take into account system availability and the repairpersons with different skill levels. To solve this problem, a new multi-mission selective maintenance and repairpersons assignment model with stochastic duration of the mission are developed. To maximize the minimum phase-mission reliability while meeting the minimum system availability, the model is transformed into an optimization problem subject to limited maintenance resources. The optimization is then realized using an analytical method based on a self-programming function and a Monte Carlo simulation method, respectively. Finally, the validity of the model and solution method approaches are verified by numerical arithmetic examples. Comparative and sensitivity analyses are made to provide proven recommendations for decision-makers.
基金supported by the National Natural Science Foundation of China(72101025,72271049),the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities,FRF-IDRY-24-024)the Hebei Natural Science Foundation(F2023501011)+1 种基金the Fundamental Research Funds for the Central Universities(FRF-TP-20-073A1)the R&D Program of Beijing Municipal Education Commission(KM202411232015).
文摘This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with one source element and multiple sink elements is considered first.Each element can con-nect with other elements within a stochastic connection ranges.The system is regarded as successful as long as the source ele-ment remains connected with all sink elements.An importance measure is proposed to evaluate the performance of non-source elements.Furthermore,to calculate the system reliability and the element importance measure,a multi-valued decision diagram based approach is structured and its complexity is analyzed.Finally,a numerical example about the signal transfer station system is illustrated to analyze the system reliability and the ele-ment importance measure.
基金Supported by the Science and Technology Research Projects of Hubei Provincial Department of Education(B2022077)。
文摘We study the distribution limit of a class of stochastic evolution equation driven by an additive-stable Non-Gaussian process in the case of α∈(1,2).We prove that,under suitable conditions,the law of the solution converges weakly to the law of a stochastic evolution equation with an additive Gaussian process.
基金supported by the National Natural Science Foundation of China(607404306646087403160904060)
文摘The problem of robust H_∞ control for uncertain neutral stochastic systems with time-varying delay is discussed.The parameter uncertaintie is assumed to be time varying norm-bounded.First,the stochastic robust stabilization of the stochastic system without disturbance input is investigated by nonlinear matrix inequality method.Then,a full-order stochastic dynamic output feedback controller is designed by solving a bilinear matrix inequality（BMI）,which ensures a prescribed stochastic robust H_∞ performance level for the resulting closed-loop system with nonzero disturbance input and for all admissible uncertainties.An illustrative example is provided to show the feasibility of the controller and the potential of the proposed technique.
基金supported by the National Natural Science Foundation of China(61773387)the China Postdoctoral Fund(2016M5909712017T100770)。
文摘A consensus-distributed fault-tolerant(CDFT)control law is proposed for a class of leader-following multi-vehicle cooperative attack(MVCA)systems in this paper.In particular,the switching communication topologies,stochastic multi-hop timevarying delays,and actuator faults are considered,which may lead to system performance degradation or on certain occasions even cause system instability.Firstly,the estimator of actuator faults for the following vehicle is designed to identify the actuator faults under a fixed topology.Then the CDFT control protocol and trajectory following error are derived by the relevant content of Lyapunov stability theory,the graph theory,and the matrix theory.The CDFT control protocol is proposed in the same manner,where a more realistic scenario is considered,in which the maximum trajectory following error and information on the switching topologies during the cooperative attack are available.Finally,numerical simulation are carried out to indicate that the proposed distributed fault-tolerant(DFT)control law is effective.
基金supported by the National Natural Science Foundation of China(60874114)
文摘Stochastic switched epidemic systems with a discrete or distributed time delay are constructed and investigated. By the Lyapunov method and lto＇s differential rule, the existence and uniqueness of global positive solution of each system is proved. And stability conditions of the disease-free equilibrium of the systems are obtained. Numerical simulations are presented to illustrate the results.
基金supported in part by the National Natural Science Foundation of China (60874045 60904030)+1 种基金the Foundation of the Education Bureau of Jiangsu Province (09KJB510019)the Natural Science Foundation of Jiangsu Province (BK2009184)
文摘A robust dissipative control problem for a class of It-type stochastic systems is discussed with Markovian jumping parameters and time-varying delay. A memoryless state feedback dissipative controller is developed based on Lyapunov-Krasovskii functional approach such that the closed-loop system is robustly stochastically stable and weakly delay-dependent （RSSWDD） and strictly （Q, S, R）-dissipative. The sufficient condition on the existence of state feedback dissipative controller is presented by linear matrix inequality （LMI）. And the desired controller can be concluded as solving a set of LMI. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed approach.
基金Projects(51804113,51434006,51874130)supported by the National Natural Science Foundation of ChinaProject(E51768)supported by the Doctoral Initiation Foundation of Hunan University of Science and Technology,China+1 种基金Project(E61610)supported by the Postdoctoral Research Foundation of Hunan University of Science and Technology,ChinaProject(E21734)supported by the Open Foundation of Work Safety Key Lab on Prevention and Control of Gas and Roof Disasters for Southern Coal Mines,China
文摘A new horn failure mechanism was constructed for tunnel faces in the soft rock mass by means of the logarithmic spiral curve. The seismic action was incorporated into the horn failure mechanism using the pseudo-static method. Considering the randomness of rock mass parameters and loads, a three-dimensional (3D) stochastic collapse model was established. Reliability analysis of seismic stability of tunnel faces was presented via the kinematical approach and the response surface method. The results show that, the reliability of tunnel faces is significantly affected by the supporting pressure, geological strength index, uniaxial compressive strength, rock bulk density and seismic forces. It is worth noting that, if the effect of seismic force was not considered, the stability of tunnel faces would be obviously overestimated. However, the correlation between horizontal and vertical seismic forces can be ignored under the condition of low calculation accuracy.
基金supported by the Natural Science Foundation of China(71471174)
文摘Developing a course of action(COA) is a key step in military planning. In most extant studies on the COA development,only the unilateral actions of friendly forces are considered. Based on stochastic games, we propose models that could deal with the complexities and uncertainties of wars. By analyzing the equilibrium state of both opponent sides, outcomes preferable to one side could be achieved by adopting the methods obtained from the proposed models. This research could help decision makers take the right COA in a state of uncertainty.
基金Projects(61603393,61741318)supported in part by the National Natural Science Foundation of ChinaProject(BK20160275)supported by the Natural Science Foundation of Jiangsu Province,China+1 种基金Project(2015M581885)supported by the Postdoctoral Science Foundation of ChinaProject(PAL-N201706)supported by the Open Project Foundation of State Key Laboratory of Synthetical Automation for Process Industries of Northeastern University,China
文摘As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configuration network(SCN)with robust technique,namely,robust SCN(RSCN).Firstly,this paper proves the universal approximation property of RSCN with weighted least squares technique.Secondly,three robust algorithms are presented by employing M-estimation with Huber loss function,M-estimation with interquartile range(IQR)and nonparametric kernel density estimation(NKDE)function respectively to set the penalty weight.Comparison experiments are first carried out based on the UCI standard data sets to verify the effectiveness of these methods,and then the data-driven PS model based on the robust algorithms are established and verified.Experimental results show that the RSCN has an excellent performance for the PS estimation.
文摘Multi-attribute decision problems where the performances of the alternatives are random variables are considered. The suggested approach grades the probabilities of preference of one alternative over another with respect to the same attribute. Based on the graded probabilistic dominance relation, the pairwise comparison information table is defined. The global preferences of the decision maker can be seen as a rough binary relation. The present paper proposes to approximate this preference relation by means of the graded probabilistic dominance relation with respect to the subsets of attributes. At last, the method is illustrated by an example.
文摘The traditional voltage stability analysis method is mostly based on the deterministic mode1.and ignores the uncertainties of bus loads,power supplies,changes in network configuration and so on.However,the great expansion of renewable power generations such as wind and solar energy in a power system has increased their uncertainty,and仃aditional techniques are limited in capturing their variable behavior.This leads to greater needs of new techniques and methodologies to properly quan tify the voltage stability of power systems.
基金supported by the UK Leverhulme Trust (F/00 120/BC)the National Natural Science Foundation of China (6082800760974029)
文摘A new fault tolerant control（FTC） via a controller reconfiguration approach for general stochastic nonlinear systems is studied.Different from the formulation of classical FTC methods,it is supposed that the measured information for the FTC is the probability density functions（PDFs） of the system output rather than its measured value.A radial basis functions（RBFs） neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network.As a result,the nonlinear FTC problem subject to dynamic relation between the input and the output PDFs can be transformed into a nonlinear FTC problem subject to dynamic relation between the control input and the weights of the RBFs neural network approximation to the output PDFs.The FTC design consists of two steps.The first step is fault detection and diagnosis（FDD）,which can produce an alarm when there is a fault in the system and also locate which component has a fault.The second step is to adapt the controller to the faulty case so that the system is able to achieve its target.A linear matrix inequality（LMI） based feasible FTC method is applied such that the fault can be detected and diagnosed.An illustrated example is included to demonstrate the efficiency of the proposed algorithm,and satisfactory results have been obtained.
基金Project(51279040)supported by the National Natural Science Foundation of China
文摘When the bi-stable stochastic resonance method was applied to enhance weak thruster fault for autonomous underwater vehicle(AUV), the enhancement performance could not satisfy the detection requirement of weak thruster fault. As for this problem, a fault feature enhancement method based on mono-stable stochastic resonance was proposed. In the method, in order to improve the enhancement performance of weak thruster fault feature, the conventional bi-stable potential function was changed to mono-stable potential function which was more suitable for aperiodic signals. Furthermore, when particle swarm optimization was adopted to adjust the parameters of mono-stable stochastic resonance system, the global convergent time would be long. An improved particle swarm optimization method was developed by changing the linear inertial weighted function as nonlinear function with cosine function, so as to reduce the global convergent time. In addition, when the conventional wavelet reconstruction method was adopted to detect the weak thruster fault, undetected fault or false alarm may occur. In order to successfully detect the weak thruster fault, a weak thruster detection method was proposed based on the integration of stochastic resonance and wavelet reconstruction. In the method, the optimal reconstruction scale was determined by comparing wavelet entropies corresponding to each decomposition scale. Finally, pool-experiments were performed on AUV with thruster fault. The effectiveness of the proposed mono-stable stochastic resonance method in enhancing fault feature and reducing the global convergent time was demonstrated in comparison with particle swarm optimization based bi-stochastic resonance method. Furthermore, the effectiveness of the proposed fault detection method was illustrated in comparison with the conventional wavelet reconstruction.
基金supported by the National Natural Science Foundation of China(6110418661273076)
文摘The stochastic convergence of the cubature Kalmanfilter with intermittent observations （CKFI） for general nonlinearstochastic systems is investigated. The Bernoulli distributed ran-dom variable is employed to describe the phenomenon of intermit-tent observations. According to the cubature sample principle, theestimation error and the error covariance matrix （ECM） of CKFIare derived by Taylor series expansion, respectively. Afterwards, itis theoretically proved that the ECM will be bounded if the obser-vation arrival probability exceeds a critical minimum observationarrival probability. Meanwhile, under proper assumption corre-sponding with real engineering situations, the stochastic stabilityof the estimation error can be guaranteed when the initial estima-tion error and the stochastic noise terms are sufficiently small. Thetheoretical conclusions are verified by numerical simulations fortwo illustrative examples; also by evaluating the tracking perfor-mance of the optical-electric target tracking system implementedby CKFI and unscented Kalman filter with intermittent observa-tions （UKFI） separately, it is demonstrated that the proposed CKFIslightly outperforms the UKFI with respect to tracking accuracy aswell as real time performance.
