Ride and handling are two paramount factors in design and development of vehicle suspension systems. Conflicting trends in ride and handling characteristics propel engineers toward employing multi-objective optimizati...Ride and handling are two paramount factors in design and development of vehicle suspension systems. Conflicting trends in ride and handling characteristics propel engineers toward employing multi-objective optimization methods capable of providing the best trade-off designs compromising both criteria simultaneously. Although many studies have been performed on multi-objective optimization of vehicle suspension system, only a few of them have used probabilistic approaches considering effects of uncertainties in the design. However, it has been proved that optimum point obtained from deterministic optimization without taking into account the effects of uncertainties may lead to high-risk points instead of optimum ones. In this work, reliability-based robust multi-objective optimization of a 5 degree of freedom （5-DOF） vehicle suspension system is performed using method of non-dominated sorting genetic algorithm-II （NSGA-II） in conjunction with Monte Carlo simulation （MCS） to obtain best designs considering both comfort and handling. Road profile is modeled as a random function using power spectral density （PSD） which is in better accordance with reality. To accommodate the robust approach, the variance of all objective functions is also considered to be minimized. Also, to take into account the reliability criterion, a reliability-based constraint is considered in the optimization. A deterministic optimization has also been performed to compare the results with probabilistic study and some other deterministic studies in the literature. In addition, sensitivity analysis has been performed to reveal the effects of different design variables on objective functions. To introduce the best trade-off points from the obtained Pareto fronts, TOPSIS method has been employed. Results show that optimum design point obtained from probabilistic optimization in this work provides better performance while demonstrating very good reliability and robustness. However, other optimum points from deterministic optimizations violate the regarded constraints in the presence of uncertainties.展开更多
Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach wa...Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.展开更多
In order to improve the fine structure inversion ability of igneous rocks for the exploration of underlying strata, based on particle swarm optimization(PSO), we have developed a method for seismic wave impedance inve...In order to improve the fine structure inversion ability of igneous rocks for the exploration of underlying strata, based on particle swarm optimization(PSO), we have developed a method for seismic wave impedance inversion. Through numerical simulation, we tested the effects of different algorithm parameters and different model parameterization methods on PSO wave impedance inversion, and analyzed the characteristics of PSO method. Under the conclusions drawn from numerical simulation, we propose the scheme of combining a cross-moving strategy based on a divided block model and high-frequency filtering technology for PSO inversion. By analyzing the inversion results of a wedge model of a pitchout coal seam and a coal coking model with igneous rock intrusion, we discuss the vertical and horizontal resolution, stability and reliability of PSO inversion. Based on the actual seismic and logging data from an igneous area, by taking a seismic profile through wells as an example, we discuss the characteristics of three inversion methods, including model-based wave impedance inversion, multi-attribute seismic inversion based on probabilistic neural network(PNN) and wave impedance inversion based on PSO.And we draw the conclusion that the inversion based on PSO method has a better result for this igneous area.展开更多
This paper presents the design of sliding mode controller for the output regulation of single input single output （SISO） nonlinear systems. The sliding surfaces are designed to force the error dynamics to follow pro...This paper presents the design of sliding mode controller for the output regulation of single input single output （SISO） nonlinear systems. The sliding surfaces are designed to force the error dynamics to follow proportional （P）, proportional integral （PI） and proportional integral derivative （PID） dynamics. The controller parameters are obtained using probabilistic particle swarm optimization technique. A judicious selection of various sliding surfaces based on the relative degree of the systems is also elaborated. A detailed comparison of the output regulation for various systems with different relative degree is presented. Numerical simulation shows the effectiveness of the proposed method and robustness of the sliding mode controller.展开更多
This work proposes an innovative approach to evaluate the functional characteristics of a heterogeneous underwater wireless acoustic sensor network(UWASN)using a stochastic model and the network connectivity criterion...This work proposes an innovative approach to evaluate the functional characteristics of a heterogeneous underwater wireless acoustic sensor network(UWASN)using a stochastic model and the network connectivity criterion.The connectivity criterion is probabilistic and considers inherently distinct groups of parameters:technical parameters that determine the network function at specific levels of the communication stack and physical parameters that describe the environment in the water area.The proposed approach enables researchers to evaluate the network characteristics in terms of energy efficiency and reliability while considering specific network and environmental parameters.Moreover,this approach is a simple and convenient tool for analyzing the effectiveness of protocols in various open systems interconnection model levels.It is possible to assess the potential capabilities of any protocol and include it in the proposed model.This work presents the results of modeling the critical characteristics of heterogeneous three-dimensional UWASNs of different scales consisting of stationary sensors and a wave glider as a mobile gateway,using specific protocols as examples.Several alternative routes for the wave glider are considered to optimize the network’s functional capabilities.Optimal trajectories of the wave glider’s movement have been determined in terms of ensuring the efficiency and reliability of the hybrid UWASN at various scales.In the context of the problem,an evaluation of different reference node placement was to ensure message transmission to a mobile gateway.The best location of reference nodes has been found.展开更多
As wind farms are commonly installed in areas with abundant wind resources,spatial dependence of wind speed among nearby wind farms should be considered when modeling a power system with large-scale wind power.In this...As wind farms are commonly installed in areas with abundant wind resources,spatial dependence of wind speed among nearby wind farms should be considered when modeling a power system with large-scale wind power.In this paper,a novel bivariate non-parametric copula,and a bivariate diffusive kernel(BDK)copula are proposed to formulate the dependence between random variables.BDK copula is then applied to higher dimension using the pair-copula method and is named as pair diffusive kernel(PDK)copula,offering flexibility to formulate the complicated dependent structure of multiple random variables.Also,a quasi-Monte Carlo method is elaborated in the sampling procedure based on the combination of the Sobol sequence and the Rosen-blatt transformation of the PDK copula,to generate correlated wind speed samples.The proposed method is applied to solve probabilistic optimal power flow(POPF)problems.The effectiveness of the BDK copula is validated in copula definitions.Then,three different data sets are used in various goodness-of-fit tests to verify the superior performance of the PDK copula,which facilitates in formulating the dependence structure of wind speeds at different wind farms.Furthermore,samples obtained from the PDK copula are used to solve POPF problems,which are modeled on three modified IEEE 57-bus power systems.Compared to the Gaussian,T,and parametric-pair copulas,the results obtained from the PDK copula are superior in formulating the complicated dependence,thus solving POPF problems.展开更多
To evaluate the impact of the randomness and correlation of photovoltaic(PV)and load on AC/DC systems with a multiport current flow controller(M-CFC),this paper proposes a probabilistic optimal power flow calculation ...To evaluate the impact of the randomness and correlation of photovoltaic(PV)and load on AC/DC systems with a multiport current flow controller(M-CFC),this paper proposes a probabilistic optimal power flow calculation for AC/DC systems,based on a nonparametric kernel density estimation.First,according to the M-CFC model,the DC power flow calculation method with M-CFC was inferred,and its influence on line loss was analyzed.Second,a nonparametric kernel density estimation with an adaptive bandwidth is used to accurately describe the probability distribution of the PV and load,and correlation samples of the PV and load are obtained by the mixed copula function.Then an optimization model that considers system loss and static security is established,and a fast nondominated sorting genetic algorithm based on the elite strategy(NSGA-II)is used to calculate the multi-objective probability optimal power flow of the AC/DC system.Finally,a case study is performed on a modified IEEE39 bus system using measured PV and load data.We verified that the nonparametric kernel density estimation with an adaptive bandwidth can better adapt to random component uncertainty,and M-CFC can improve the static security of the system.展开更多
We exploit optimal probabilistic cloning to rederive the JS limit.Dependent on the formulation given by the optimal probabilistic cloning,the explicit transformation of a measure of the JS limit is presented.Based on ...We exploit optimal probabilistic cloning to rederive the JS limit.Dependent on the formulation given by the optimal probabilistic cloning,the explicit transformation of a measure of the JS limit is presented.Based on linear optical devices,we propose an experimentally feasible scheme to implement the JS limit measure of a general pair of two nonorthogonal quantum states.The success probability of the proposed scheme is unity.展开更多
文摘Ride and handling are two paramount factors in design and development of vehicle suspension systems. Conflicting trends in ride and handling characteristics propel engineers toward employing multi-objective optimization methods capable of providing the best trade-off designs compromising both criteria simultaneously. Although many studies have been performed on multi-objective optimization of vehicle suspension system, only a few of them have used probabilistic approaches considering effects of uncertainties in the design. However, it has been proved that optimum point obtained from deterministic optimization without taking into account the effects of uncertainties may lead to high-risk points instead of optimum ones. In this work, reliability-based robust multi-objective optimization of a 5 degree of freedom （5-DOF） vehicle suspension system is performed using method of non-dominated sorting genetic algorithm-II （NSGA-II） in conjunction with Monte Carlo simulation （MCS） to obtain best designs considering both comfort and handling. Road profile is modeled as a random function using power spectral density （PSD） which is in better accordance with reality. To accommodate the robust approach, the variance of all objective functions is also considered to be minimized. Also, to take into account the reliability criterion, a reliability-based constraint is considered in the optimization. A deterministic optimization has also been performed to compare the results with probabilistic study and some other deterministic studies in the literature. In addition, sensitivity analysis has been performed to reveal the effects of different design variables on objective functions. To introduce the best trade-off points from the obtained Pareto fronts, TOPSIS method has been employed. Results show that optimum design point obtained from probabilistic optimization in this work provides better performance while demonstrating very good reliability and robustness. However, other optimum points from deterministic optimizations violate the regarded constraints in the presence of uncertainties.
文摘Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.
基金provided by the National Science and Technology Major Project(No.2011ZX05004-004)China National Petroleum Corporation Key Projects(No.2014E2105)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘In order to improve the fine structure inversion ability of igneous rocks for the exploration of underlying strata, based on particle swarm optimization(PSO), we have developed a method for seismic wave impedance inversion. Through numerical simulation, we tested the effects of different algorithm parameters and different model parameterization methods on PSO wave impedance inversion, and analyzed the characteristics of PSO method. Under the conclusions drawn from numerical simulation, we propose the scheme of combining a cross-moving strategy based on a divided block model and high-frequency filtering technology for PSO inversion. By analyzing the inversion results of a wedge model of a pitchout coal seam and a coal coking model with igneous rock intrusion, we discuss the vertical and horizontal resolution, stability and reliability of PSO inversion. Based on the actual seismic and logging data from an igneous area, by taking a seismic profile through wells as an example, we discuss the characteristics of three inversion methods, including model-based wave impedance inversion, multi-attribute seismic inversion based on probabilistic neural network(PNN) and wave impedance inversion based on PSO.And we draw the conclusion that the inversion based on PSO method has a better result for this igneous area.
文摘This paper presents the design of sliding mode controller for the output regulation of single input single output （SISO） nonlinear systems. The sliding surfaces are designed to force the error dynamics to follow proportional （P）, proportional integral （PI） and proportional integral derivative （PID） dynamics. The controller parameters are obtained using probabilistic particle swarm optimization technique. A judicious selection of various sliding surfaces based on the relative degree of the systems is also elaborated. A detailed comparison of the output regulation for various systems with different relative degree is presented. Numerical simulation shows the effectiveness of the proposed method and robustness of the sliding mode controller.
基金partially funded by the Ministry of Science and Higher Education of the Russian Federation as a part of World-class Research Center Program:Advanced Digital Technologies(contract No.075-15-2022-312 dated 20 April 2022).
文摘This work proposes an innovative approach to evaluate the functional characteristics of a heterogeneous underwater wireless acoustic sensor network(UWASN)using a stochastic model and the network connectivity criterion.The connectivity criterion is probabilistic and considers inherently distinct groups of parameters:technical parameters that determine the network function at specific levels of the communication stack and physical parameters that describe the environment in the water area.The proposed approach enables researchers to evaluate the network characteristics in terms of energy efficiency and reliability while considering specific network and environmental parameters.Moreover,this approach is a simple and convenient tool for analyzing the effectiveness of protocols in various open systems interconnection model levels.It is possible to assess the potential capabilities of any protocol and include it in the proposed model.This work presents the results of modeling the critical characteristics of heterogeneous three-dimensional UWASNs of different scales consisting of stationary sensors and a wave glider as a mobile gateway,using specific protocols as examples.Several alternative routes for the wave glider are considered to optimize the network’s functional capabilities.Optimal trajectories of the wave glider’s movement have been determined in terms of ensuring the efficiency and reliability of the hybrid UWASN at various scales.In the context of the problem,an evaluation of different reference node placement was to ensure message transmission to a mobile gateway.The best location of reference nodes has been found.
基金supported by Key-Area Research and Development Program of Guangdong Province(No.2020B010166004)the National Natural Science Foundation of China(No.52077081).
文摘As wind farms are commonly installed in areas with abundant wind resources,spatial dependence of wind speed among nearby wind farms should be considered when modeling a power system with large-scale wind power.In this paper,a novel bivariate non-parametric copula,and a bivariate diffusive kernel(BDK)copula are proposed to formulate the dependence between random variables.BDK copula is then applied to higher dimension using the pair-copula method and is named as pair diffusive kernel(PDK)copula,offering flexibility to formulate the complicated dependent structure of multiple random variables.Also,a quasi-Monte Carlo method is elaborated in the sampling procedure based on the combination of the Sobol sequence and the Rosen-blatt transformation of the PDK copula,to generate correlated wind speed samples.The proposed method is applied to solve probabilistic optimal power flow(POPF)problems.The effectiveness of the BDK copula is validated in copula definitions.Then,three different data sets are used in various goodness-of-fit tests to verify the superior performance of the PDK copula,which facilitates in formulating the dependence structure of wind speeds at different wind farms.Furthermore,samples obtained from the PDK copula are used to solve POPF problems,which are modeled on three modified IEEE 57-bus power systems.Compared to the Gaussian,T,and parametric-pair copulas,the results obtained from the PDK copula are superior in formulating the complicated dependence,thus solving POPF problems.
基金supported by the National Natural Science Foundation of China(Grant No.51677023).
文摘To evaluate the impact of the randomness and correlation of photovoltaic(PV)and load on AC/DC systems with a multiport current flow controller(M-CFC),this paper proposes a probabilistic optimal power flow calculation for AC/DC systems,based on a nonparametric kernel density estimation.First,according to the M-CFC model,the DC power flow calculation method with M-CFC was inferred,and its influence on line loss was analyzed.Second,a nonparametric kernel density estimation with an adaptive bandwidth is used to accurately describe the probability distribution of the PV and load,and correlation samples of the PV and load are obtained by the mixed copula function.Then an optimization model that considers system loss and static security is established,and a fast nondominated sorting genetic algorithm based on the elite strategy(NSGA-II)is used to calculate the multi-objective probability optimal power flow of the AC/DC system.Finally,a case study is performed on a modified IEEE39 bus system using measured PV and load data.We verified that the nonparametric kernel density estimation with an adaptive bandwidth can better adapt to random component uncertainty,and M-CFC can improve the static security of the system.
基金supported by the National Natural Science Foundation of China (Grant Nos. 11074002,61073048 and 11104057)the Natural Science Foundation of the Education Department of Anhui Province of China(Grant Nos. KJ2010ZD08 and KJ2012A245)the Postgraduate Program of Huainan Normal University
文摘We exploit optimal probabilistic cloning to rederive the JS limit.Dependent on the formulation given by the optimal probabilistic cloning,the explicit transformation of a measure of the JS limit is presented.Based on linear optical devices,we propose an experimentally feasible scheme to implement the JS limit measure of a general pair of two nonorthogonal quantum states.The success probability of the proposed scheme is unity.
