This paper proposes a modification of the Forrestal-Warren perforation model aimed at extending its applicability range to intermediately-thick high-hardness armor steel plates.When impacted by armorpiercing projectil...This paper proposes a modification of the Forrestal-Warren perforation model aimed at extending its applicability range to intermediately-thick high-hardness armor steel plates.When impacted by armorpiercing projectiles,these plates tend to fail through adiabatic shear plugging which significantly reduces their ballistic resistance.To address this effect,an approach for determining effective thickness was defined and incorporated into the predictive model.Ballistic impact tests were performed to assess the modification's validity,in which ARMOX 500T steel plates were subjected to perpendicular impacts from 7.62×39 mm steel-cored rounds under various velocities.Frequent target failure by soft plugging was observed,as well as the brittle shatter of the hard steel core.Key properties of the recovered plugs including their mass,length and diameter were measured and reported along with the projectiles'residual velocities.Additionally,independent data from the open literature were included in the analysis for further validation.The original Forrestal-Warren model and the novel effective thickness modification were then used to establish the relationship between impact and residual velocities,as well as to determine the ballistic limit velocity.The comparison revealed that the proposed approach significantly improves the model's accuracy,showing a strong correlation with experimental data and reducing deviations to within a few percent.This enhancement highlights the potential of the effective thickness term,which could also be applied to other predictive models to extend their applicability range.Further exploration into other armor steels and impact conditions is recommended to assess the method's versatility.展开更多
This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hype...This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.展开更多
This paper presents a Nonlinear Model Predictive Controller(NMPC)for the path following of autonomous vehicles and an algorithm to adaptively adjust the preview distance.The prediction model includes vehicle dynamics,...This paper presents a Nonlinear Model Predictive Controller(NMPC)for the path following of autonomous vehicles and an algorithm to adaptively adjust the preview distance.The prediction model includes vehicle dynamics,path following dynamics,and system input dynamics.The single-track vehicle model considers the vehicle’s coupled lateral and longitudinal dynamics,as well as nonlinear tire forces.The tracking error dynamics are derived based on the curvilinear coordinates.The cost function is designed to minimize path tracking errors and control effort while considering constraints such as actuator bounds and tire grip limits.An algorithm that utilizes the optimal preview distance vector to query the corresponding reference curvature and reference speed.The length of the preview path is adaptively adjusted based on the vehicle speed,heading error,and path curvature.We validate the controller performance in a simulation environment with the autonomous racing scenario.The simulation results show that the vehicle accurately follows the highly dynamic path with small tracking errors.The maximum preview distance can be prior estimated and guidance the selection of the prediction horizon for NMPC.展开更多
As a typical steel,the fatigue of marine high-strength steels has been emphasized by scholars.In this paper,the fatigue performance and crack growth mechanism of a high-strength steel for ships are investigated by exp...As a typical steel,the fatigue of marine high-strength steels has been emphasized by scholars.In this paper,the fatigue performance and crack growth mechanism of a high-strength steel for ships are investigated by experimental methods.First,the fatigue threshold test and fatigue crack growth rate test of this high-strength steel under different stress ratios were carried out.The influence of stress ratio on the fatigue properties of this steel was analyzed.Secondly,scanning electron microscope was used to analyze the crack growth specimen section of this steel.The crack growth and failure mechanism of this steel were revealed.Finally,based on the above research results,the stress ratio effect of high-strength steel was investigated from the perspectives of crack closure and driving force.Considering the fatigue behavior in the near-threshold stage and the destabilization stage,a fatigue crack growth behavior prediction model of highstrength steel was established.The accuracy of the model was verified by test data.Moreover,the applicability of the modified model to various materials and its excellent predictive ability were verified through comparison with literature data and existing models.展开更多
Bonding quality at the interface of solid propellant grains is crucial for the reliability and safety of solid rocket motors.Although bonding reliability is influenced by numerous factors,the lack of quantitative char...Bonding quality at the interface of solid propellant grains is crucial for the reliability and safety of solid rocket motors.Although bonding reliability is influenced by numerous factors,the lack of quantitative characterization of interface debonding mechanisms and the challenge of identifying key factors have made precise control of process variables difficult,resulting in unpredictable failure risks.This paper presents an improved fuzzy failure probability evaluation method that combines fuzzy fault tree analysis with expert knowledge,transforming process data into fuzzy failure probability to accurately assess debonding probabilities.The predictive model is constructed through a general regression neural network and optimized using the particle swarm optimization algorithm.Sensitivity analysis is conducted to identify key decision variables,including normal force,grain rotation speed,and adhesive weight,which are verified experimentally.Compared with classical models,the maximum error margin of the constructed reliability prediction model is only 0.02%,and it has high stability.The experimental results indicate that the main factors affecting debonding are processing roughness and coating uniformity.Controlling the key decision variable as the median resulted in a maximum increase of 200.7%in bonding strength.The feasibility of the improved method has been verified,confirming that identifying key decision variables has the ability to improve bonding reliability.The proposed method simplifies the evaluation of propellant interface bonding reliability under complex conditions by quantifying the relationship between process parameters and failure risk,enabling targeted management of key decision variables.展开更多
The ductile-to-brittle transition temperature(DBTT)of high strength steels can be optimized by tailoring microstructure and crystallographic orientation characteristics,where the start cooling temperature plays a key ...The ductile-to-brittle transition temperature(DBTT)of high strength steels can be optimized by tailoring microstructure and crystallographic orientation characteristics,where the start cooling temperature plays a key role.In this work,X70 steels with different start cooling temperatures were prepared through thermo-mechanical control process.The quasi-polygonal ferrite(QF),granular bainite(GB),bainitic ferrite(BF)and martensite-austenite constituents were formed at the start cooling temperatures of 780℃(C1),740℃(C2)and 700℃(C3).As start cooling temperature decreased,the amount of GB decreased,the microstructure of QF and BF increased.Microstructure characteristics of the three samples,such as high-angle grain boundaries(HAGBs),MA constituents and crystallographic orientation,also varied with the start cooling temperatures.C2 sample had the lowest DBTT value(−86℃)for its highest fraction of HAGBs,highest content of<110>oriented grains and lowest content of<001>oriented grains parallel to TD.The high density of{332}<113>and low density of rotated cube{001}<110>textures also contributed to the best impact toughness of C2 sample.In addition,a modified model was used in this paper to quantitatively predict the approximate DBTT value of steels.展开更多
This paper proposed a new libration decoupling analytical speed function(LD-ASF)in lieu of the classic analytical speed function to control the climber's speed along a partial space elevator to improve libration s...This paper proposed a new libration decoupling analytical speed function(LD-ASF)in lieu of the classic analytical speed function to control the climber's speed along a partial space elevator to improve libration stability in cargo transportation.The LD-ASF is further optimized for payload transportation efficiency by a novel coordinate game theory to balance competing control objectives among payload transport speed,stable end body's libration,and overall control input via model predictive control.The transfer period is divided into several sections to reduce computational burden.The validity and efficacy of the proposed LD-ASF and coordinate game-based model predictive control are demonstrated by computer simulation.Numerical results reveal that the optimized LD-ASF results in higher transportation speed,stable end body's libration,lower thrust fuel consumption,and more flexible optimization space than the classic analytical speed function.展开更多
In grey system theory,the studies in the field of grey prediction model are focused on real number sequences,rather than grey number ones.Hereby,a prediction model based on interval grey number sequences is proposed.B...In grey system theory,the studies in the field of grey prediction model are focused on real number sequences,rather than grey number ones.Hereby,a prediction model based on interval grey number sequences is proposed.By mining the geometric features of interval grey number sequences on a two-dimensional surface,all the interval grey numbers are converted into real numbers by means of certain algorithm,and then the prediction model is established based on those real number sequences.The entire process avoids the algebraic operations of grey number,and the prediction problem of interval grey number is usefully solved.Ultimately,through an example＇s program simulation,the validity and practicability of this novel model are verified.展开更多
Pre-knowledge of machined surface roughness is the key to improve whole machining efficiency and meanwhile reduce the expenditure in machining optical glass components.In order to predict the surface roughness in ultr...Pre-knowledge of machined surface roughness is the key to improve whole machining efficiency and meanwhile reduce the expenditure in machining optical glass components.In order to predict the surface roughness in ultrasonic vibration assisted grinding of brittle materials,the surface morphologies of grinding wheel were obtained firstly in the present work,the grinding wheel model was developed and the abrasive trajectories in ultrasonic vibration assisted grinding were also investigated,the theoretical model for surface roughness was developed based on the above analysis.The prediction model was developed by using Gaussian processing regression(GPR)due to the influence of brittle fracture on machined surface roughness.In order to validate both the proposed theoretical and GPR models,32sets of experiments of ultrasonic vibration assisted grinding of BK7optical glass were carried out.Experimental results show that the average relative errors of the theoretical model and GPR prediction model are13.11%and8.12%,respectively.The GPR prediction results can match well with the experimental results.展开更多
This study aims to reveal the macroscopic permanent deformation(PD)behavior and the internal structural evolution of construction and demolition waste(CDW)under loading.Firstly,the initial matric suction of CDW was me...This study aims to reveal the macroscopic permanent deformation(PD)behavior and the internal structural evolution of construction and demolition waste(CDW)under loading.Firstly,the initial matric suction of CDW was measured by the filter paper method.Secondly,the PD of CDW with different humidity and stress states was investigated by repeated load triaxial tests,and a comprehensive prediction model was established.Finally,the discrete element method was performed to analyze the internal structural evolution of CDW during deformation.These results showed that the VAN-GENUCHTEN model could describe the soil-water characteristic curve of CDW well.The PD increases with the increase of the deviator stress and the number of cyclic loading,but the opposite trend was observed when the initial matric suction and confining pressure increased.The proposed model in this study provides a satisfactory prediction of PD.The discrete element method could accurately simulate the macroscopic PD of CDW,and the shear force,interlock force and sliding content increase with the increase of deviator stress during the deformation.The research could provide useful reference for the deformation stability analysis of CDW under cyclic loading.展开更多
For a class of linear discrete-time systems that is subject to randomly occurred networked packet loss in industrial cyber physical systems, a novel robust model predictive control method with active compensation mech...For a class of linear discrete-time systems that is subject to randomly occurred networked packet loss in industrial cyber physical systems, a novel robust model predictive control method with active compensation mechanism was proposed. The probability distribution of packet loss is described as the Bernoulli distributed white sequences. By using the Lyapunov stability theory, the existing sufficient conditions of the controller are derived from solving a group of linear matrix inequalities. Moreover, dropout-rate with uncertainty and unknown dropout-rate are also considered, which can greatly reduce the conservativeness of the controller. The designed robust model predictive control method not only efficiently eliminates the negative effects of the networked data loss in industrial cyber physical systems but also ensures the stability of closed-loop system. Two examples were provided to illustrate the superiority and effectiveness of the proposed method.展开更多
In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction models, the equivalence and unbiasedness of grey prediction models are analyzed and verified. The results sh...In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction models, the equivalence and unbiasedness of grey prediction models are analyzed and verified. The results show that all the grey prediction models that are strictly derived from x^（0）（k） ＋az^（1）（k） = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homogeneous exponential sequence can be accomplished. However, the models derived from dx^（1）/dt ＋ ax^（1）= b are only close to those derived from x^（0）（k） ＋ az^（1）（k） = b provided that ｜a｜ has to satisfy｜a｜ 0.1; neither could the unbiased simulation for the homogeneous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.展开更多
A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established...A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.展开更多
Most of the existing multivariable grey models are based on the 1-order derivative and 1-order accumulation, which makes the parameters unable to be adjusted according to the data characteristics of the actual problem...Most of the existing multivariable grey models are based on the 1-order derivative and 1-order accumulation, which makes the parameters unable to be adjusted according to the data characteristics of the actual problems. The results about fractional derivative multivariable grey models are very few at present. In this paper, a multivariable Caputo fractional derivative grey model with convolution integral CFGMC(q, N) is proposed. First, the Caputo fractional difference is used to discretize the model, and the least square method is used to solve the parameters. The orders of accumulations and differential equations are determined by using particle swarm optimization(PSO). Then, the analytical solution of the model is obtained by using the Laplace transform, and the convergence and divergence of series in analytical solutions are also discussed. Finally, the CFGMC(q, N) model is used to predict the municipal solid waste(MSW). Compared with other competition models, the model has the best prediction effect. This study enriches the model form of the multivariable grey model, expands the scope of application, and provides a new idea for the development of fractional derivative grey model.展开更多
Residual stress distributions in 7075 aluminum alloy thick plates with different thicknesses and different quenching speeds were measured. A shape function of stress distribution was proposed based on the internal str...Residual stress distributions in 7075 aluminum alloy thick plates with different thicknesses and different quenching speeds were measured. A shape function of stress distribution was proposed based on the internal stress distribution characteristics of aluminum alloy. Using nonlinear regression technology,the function between stress value of key points on internal stress curve and surface stress of the plate was obtained. Based on the measured surface stress,stress value of key points and stress distribution shape,the internal stress distribution can be reconstructed. The experiments show that the model is of good engineering practicality.展开更多
Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs ...Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs based MPC was derived, and then the necessary and sufficient stability condition for MPC closed loop was given according to SVM model, and finally a method of judging the discrepancy between SVM model and the actual plant was presented, and consequently the constraint sets, which can guarantee that the stability condition is still robust for model/plant mismatch within some given bounds, were obtained by applying small-gain theorem. Simulation experiments show the proposed stability condition and robust constraint sets can provide a convenient way of adjusting controller parameters to ensure a closed-loop with larger stable margin.展开更多
This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method ...This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.展开更多
To predict the erosion and abrasion of high bore pressure tank gun barrel, the least square support vector machine (LSSVM) algorithm was used. Based on the gun firing test data, the prediction model for barrel's e...To predict the erosion and abrasion of high bore pressure tank gun barrel, the least square support vector machine (LSSVM) algorithm was used. Based on the gun firing test data, the prediction model for barrel's erosion and abrasion was established. It was adopted to predict the wear increment of gun barrel. The results show that the prediction values given by the model coincide with the measured data better, and the model can predict the barrel's wear accurately and rapidly.展开更多
The structural health status of Hunan Road Bridge during its two-year service period from April 2015 to April 2017 was studied based on monitored data.The Hunan Road Bridge is the widest concrete self-anchored suspens...The structural health status of Hunan Road Bridge during its two-year service period from April 2015 to April 2017 was studied based on monitored data.The Hunan Road Bridge is the widest concrete self-anchored suspension bridge in China at present.Its structural changes and safety were evaluated using the health monitoring data,which included deformations,detailed stresses,and vibration characteristics.The influences of the single and dual effects comprising the ambient temperature changes and concrete shrinkage and creep(S&C)were analyzed based on the measured data.The ANSYS beam finite element model was established and validated by the measured bridge completion state.The comparative analyses of the prediction results of long-term concrete S&C effects were conducted using CEB-FIP 90 and B3 prediction models.The age-adjusted effective modulus method was adopted to simulate the aging behavior of concrete.Prestress relaxation was considered in the stepwise calculation.The results show that the transverse deviations of the towers are noteworthy.The spatial effect of the extra-wide girder is significant,as the compressive stress variations at the girder were uneven along the transverse direction.General increase and decrease in the girder compressive stresses were caused by seasonal ambient warming and cooling,respectively.The temperature gradient effects in the main girder were significant.Comparisons with the measured data showed that more accurate prediction results were obtained with the B3 prediction model,which can consider the concrete material parameters,than with the CEB-FIP 90 model.Significant deflection of the midspan girder in the middle region will be caused by the deviations of the cable anchoring positions at the girder ends and tower tops toward the midspan due to concrete S&C.The increase in the compressive stresses at the top plate and decrease in the stresses at the bottom plate at the middle midspan will be significant.The pre-deviations of the towers toward the sidespan and pre-lift of the midspan girder can reduce the adverse influences of concrete S&C on the structural health of the self-anchored suspension bridge with extra-wide concrete girder.展开更多
An improved model predictive control algorithm is proposed for Hammerstein-Wiener nonlinear systems.The proposed synthesis algorithm contains two parts:offline design the polytopic invariant sets,and online solve the ...An improved model predictive control algorithm is proposed for Hammerstein-Wiener nonlinear systems.The proposed synthesis algorithm contains two parts:offline design the polytopic invariant sets,and online solve the min-max optimization problem.The polytopic invariant set is adopted to replace the traditional ellipsoid invariant set.And the parameter-correlation nonlinear control law is designed to replace the traditional linear control law.Consequently,the terminal region is enlarged and the control effect is improved.Simulation and experiment are used to verify the validity of the wind tunnel flow field control algorithm.展开更多
基金supported by the Ministry of Science,Technological Development and Innovation of the Republic of Serbia,through the Contract no.451-03-65/2024-03/200105
文摘This paper proposes a modification of the Forrestal-Warren perforation model aimed at extending its applicability range to intermediately-thick high-hardness armor steel plates.When impacted by armorpiercing projectiles,these plates tend to fail through adiabatic shear plugging which significantly reduces their ballistic resistance.To address this effect,an approach for determining effective thickness was defined and incorporated into the predictive model.Ballistic impact tests were performed to assess the modification's validity,in which ARMOX 500T steel plates were subjected to perpendicular impacts from 7.62×39 mm steel-cored rounds under various velocities.Frequent target failure by soft plugging was observed,as well as the brittle shatter of the hard steel core.Key properties of the recovered plugs including their mass,length and diameter were measured and reported along with the projectiles'residual velocities.Additionally,independent data from the open literature were included in the analysis for further validation.The original Forrestal-Warren model and the novel effective thickness modification were then used to establish the relationship between impact and residual velocities,as well as to determine the ballistic limit velocity.The comparison revealed that the proposed approach significantly improves the model's accuracy,showing a strong correlation with experimental data and reducing deviations to within a few percent.This enhancement highlights the potential of the effective thickness term,which could also be applied to other predictive models to extend their applicability range.Further exploration into other armor steels and impact conditions is recommended to assess the method's versatility.
基金supported by the National Natural Science Foundation of China(12072090).
文摘This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.
基金“National Science and Technology Council”(NSTC 111-2221-E-027-088)。
文摘This paper presents a Nonlinear Model Predictive Controller(NMPC)for the path following of autonomous vehicles and an algorithm to adaptively adjust the preview distance.The prediction model includes vehicle dynamics,path following dynamics,and system input dynamics.The single-track vehicle model considers the vehicle’s coupled lateral and longitudinal dynamics,as well as nonlinear tire forces.The tracking error dynamics are derived based on the curvilinear coordinates.The cost function is designed to minimize path tracking errors and control effort while considering constraints such as actuator bounds and tire grip limits.An algorithm that utilizes the optimal preview distance vector to query the corresponding reference curvature and reference speed.The length of the preview path is adaptively adjusted based on the vehicle speed,heading error,and path curvature.We validate the controller performance in a simulation environment with the autonomous racing scenario.The simulation results show that the vehicle accurately follows the highly dynamic path with small tracking errors.The maximum preview distance can be prior estimated and guidance the selection of the prediction horizon for NMPC.
文摘As a typical steel,the fatigue of marine high-strength steels has been emphasized by scholars.In this paper,the fatigue performance and crack growth mechanism of a high-strength steel for ships are investigated by experimental methods.First,the fatigue threshold test and fatigue crack growth rate test of this high-strength steel under different stress ratios were carried out.The influence of stress ratio on the fatigue properties of this steel was analyzed.Secondly,scanning electron microscope was used to analyze the crack growth specimen section of this steel.The crack growth and failure mechanism of this steel were revealed.Finally,based on the above research results,the stress ratio effect of high-strength steel was investigated from the perspectives of crack closure and driving force.Considering the fatigue behavior in the near-threshold stage and the destabilization stage,a fatigue crack growth behavior prediction model of highstrength steel was established.The accuracy of the model was verified by test data.Moreover,the applicability of the modified model to various materials and its excellent predictive ability were verified through comparison with literature data and existing models.
基金supported in part by the Equipment Development Pre-research Project funded by Equipment Development Department,PRC under Grant No.50923010501Fundamental Research Program of Shenyang Institute of Automation(SIA),Chinese Academy of Sciencess under Grant No.355060201。
文摘Bonding quality at the interface of solid propellant grains is crucial for the reliability and safety of solid rocket motors.Although bonding reliability is influenced by numerous factors,the lack of quantitative characterization of interface debonding mechanisms and the challenge of identifying key factors have made precise control of process variables difficult,resulting in unpredictable failure risks.This paper presents an improved fuzzy failure probability evaluation method that combines fuzzy fault tree analysis with expert knowledge,transforming process data into fuzzy failure probability to accurately assess debonding probabilities.The predictive model is constructed through a general regression neural network and optimized using the particle swarm optimization algorithm.Sensitivity analysis is conducted to identify key decision variables,including normal force,grain rotation speed,and adhesive weight,which are verified experimentally.Compared with classical models,the maximum error margin of the constructed reliability prediction model is only 0.02%,and it has high stability.The experimental results indicate that the main factors affecting debonding are processing roughness and coating uniformity.Controlling the key decision variable as the median resulted in a maximum increase of 200.7%in bonding strength.The feasibility of the improved method has been verified,confirming that identifying key decision variables has the ability to improve bonding reliability.The proposed method simplifies the evaluation of propellant interface bonding reliability under complex conditions by quantifying the relationship between process parameters and failure risk,enabling targeted management of key decision variables.
基金Project(2018XK2301) supported by the Change-Zhu-Tan National Independent Innavation Demonstration Zone Special Program,China。
文摘The ductile-to-brittle transition temperature(DBTT)of high strength steels can be optimized by tailoring microstructure and crystallographic orientation characteristics,where the start cooling temperature plays a key role.In this work,X70 steels with different start cooling temperatures were prepared through thermo-mechanical control process.The quasi-polygonal ferrite(QF),granular bainite(GB),bainitic ferrite(BF)and martensite-austenite constituents were formed at the start cooling temperatures of 780℃(C1),740℃(C2)and 700℃(C3).As start cooling temperature decreased,the amount of GB decreased,the microstructure of QF and BF increased.Microstructure characteristics of the three samples,such as high-angle grain boundaries(HAGBs),MA constituents and crystallographic orientation,also varied with the start cooling temperatures.C2 sample had the lowest DBTT value(−86℃)for its highest fraction of HAGBs,highest content of<110>oriented grains and lowest content of<001>oriented grains parallel to TD.The high density of{332}<113>and low density of rotated cube{001}<110>textures also contributed to the best impact toughness of C2 sample.In addition,a modified model was used in this paper to quantitatively predict the approximate DBTT value of steels.
基金funded by the National Natural Science Foundation of China(12102487)Basic and Applied Basic Research Foundation of Guangdong Province,China(2023A1515012339)+1 种基金Shenzhen Science and Technology Program(ZDSYS20210623091808026)the Discovery Grant(RGPIN-2024-06290)of the Natural Sciences and Engineering Research Council of Canada。
文摘This paper proposed a new libration decoupling analytical speed function(LD-ASF)in lieu of the classic analytical speed function to control the climber's speed along a partial space elevator to improve libration stability in cargo transportation.The LD-ASF is further optimized for payload transportation efficiency by a novel coordinate game theory to balance competing control objectives among payload transport speed,stable end body's libration,and overall control input via model predictive control.The transfer period is divided into several sections to reduce computational burden.The validity and efficacy of the proposed LD-ASF and coordinate game-based model predictive control are demonstrated by computer simulation.Numerical results reveal that the optimized LD-ASF results in higher transportation speed,stable end body's libration,lower thrust fuel consumption,and more flexible optimization space than the classic analytical speed function.
基金supported by the National Natural Science Foundation of China(7084001290924022)the Ph.D.Thesis Innovation and Excellent Foundation of Nanjing University of Aeronautics and Astronautics(2010)
文摘In grey system theory,the studies in the field of grey prediction model are focused on real number sequences,rather than grey number ones.Hereby,a prediction model based on interval grey number sequences is proposed.By mining the geometric features of interval grey number sequences on a two-dimensional surface,all the interval grey numbers are converted into real numbers by means of certain algorithm,and then the prediction model is established based on those real number sequences.The entire process avoids the algebraic operations of grey number,and the prediction problem of interval grey number is usefully solved.Ultimately,through an example＇s program simulation,the validity and practicability of this novel model are verified.
基金Project(51375119) supported by the National Natural Science Foundation of China
文摘Pre-knowledge of machined surface roughness is the key to improve whole machining efficiency and meanwhile reduce the expenditure in machining optical glass components.In order to predict the surface roughness in ultrasonic vibration assisted grinding of brittle materials,the surface morphologies of grinding wheel were obtained firstly in the present work,the grinding wheel model was developed and the abrasive trajectories in ultrasonic vibration assisted grinding were also investigated,the theoretical model for surface roughness was developed based on the above analysis.The prediction model was developed by using Gaussian processing regression(GPR)due to the influence of brittle fracture on machined surface roughness.In order to validate both the proposed theoretical and GPR models,32sets of experiments of ultrasonic vibration assisted grinding of BK7optical glass were carried out.Experimental results show that the average relative errors of the theoretical model and GPR prediction model are13.11%and8.12%,respectively.The GPR prediction results can match well with the experimental results.
基金Project(52025085)supported by the National Science Fund for Distinguished Young Scholars,ChinaProjects(51927814,51878078)supported by the National Natural Science Foundation of China+3 种基金Project(2018-025)supported by the Training Program for High-level Technical Personnel in Transportation Industry,ChinaProject(CTKY-PTRC 2018-003)supported by the Design Theory,Method and Demonstration of Durability Asphalt Pavement Based on Heavy-duty Traffic Conditions in Shanghai Area,ChinaProject(2020RC4048)supported by the Science and Technology Innovation Program of Hunan Province,ChinaProject(SJCX202001)supported by the Construction Project for Graduate Students of Changsha University of Science&Technology,China。
文摘This study aims to reveal the macroscopic permanent deformation(PD)behavior and the internal structural evolution of construction and demolition waste(CDW)under loading.Firstly,the initial matric suction of CDW was measured by the filter paper method.Secondly,the PD of CDW with different humidity and stress states was investigated by repeated load triaxial tests,and a comprehensive prediction model was established.Finally,the discrete element method was performed to analyze the internal structural evolution of CDW during deformation.These results showed that the VAN-GENUCHTEN model could describe the soil-water characteristic curve of CDW well.The PD increases with the increase of the deviator stress and the number of cyclic loading,but the opposite trend was observed when the initial matric suction and confining pressure increased.The proposed model in this study provides a satisfactory prediction of PD.The discrete element method could accurately simulate the macroscopic PD of CDW,and the shear force,interlock force and sliding content increase with the increase of deviator stress during the deformation.The research could provide useful reference for the deformation stability analysis of CDW under cyclic loading.
基金Project(61673199)supported by the National Natural Science Foundation of ChinaProject(ICT1800400)supported by the Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China
文摘For a class of linear discrete-time systems that is subject to randomly occurred networked packet loss in industrial cyber physical systems, a novel robust model predictive control method with active compensation mechanism was proposed. The probability distribution of packet loss is described as the Bernoulli distributed white sequences. By using the Lyapunov stability theory, the existing sufficient conditions of the controller are derived from solving a group of linear matrix inequalities. Moreover, dropout-rate with uncertainty and unknown dropout-rate are also considered, which can greatly reduce the conservativeness of the controller. The designed robust model predictive control method not only efficiently eliminates the negative effects of the networked data loss in industrial cyber physical systems but also ensures the stability of closed-loop system. Two examples were provided to illustrate the superiority and effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(1147105951375517+5 种基金71271226)the China Postdoctoral Science Foundation Funded Project(2014M560712)Chongqing Frontier and Applied Basic Research Project(cstc2014jcyj A00024)the Ministry of Education of Humanities and Social Sciences Youth Foundation(14YJAZH033)the Chongqing Municipal Education Scientific Planning Project(2012-GX-142)the Higher School Teaching Reform Research Project in Chongqing(1202010)
文摘In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction models, the equivalence and unbiasedness of grey prediction models are analyzed and verified. The results show that all the grey prediction models that are strictly derived from x^（0）（k） ＋az^（1）（k） = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homogeneous exponential sequence can be accomplished. However, the models derived from dx^（1）/dt ＋ ax^（1）= b are only close to those derived from x^（0）（k） ＋ az^（1）（k） = b provided that ｜a｜ has to satisfy｜a｜ 0.1; neither could the unbiased simulation for the homogeneous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.
文摘A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.
基金supported by the National Natural Science Foundation of China (51479151,61403288)。
文摘Most of the existing multivariable grey models are based on the 1-order derivative and 1-order accumulation, which makes the parameters unable to be adjusted according to the data characteristics of the actual problems. The results about fractional derivative multivariable grey models are very few at present. In this paper, a multivariable Caputo fractional derivative grey model with convolution integral CFGMC(q, N) is proposed. First, the Caputo fractional difference is used to discretize the model, and the least square method is used to solve the parameters. The orders of accumulations and differential equations are determined by using particle swarm optimization(PSO). Then, the analytical solution of the model is obtained by using the Laplace transform, and the convergence and divergence of series in analytical solutions are also discussed. Finally, the CFGMC(q, N) model is used to predict the municipal solid waste(MSW). Compared with other competition models, the model has the best prediction effect. This study enriches the model form of the multivariable grey model, expands the scope of application, and provides a new idea for the development of fractional derivative grey model.
基金Projects(2005CB623708, 2010CB731703) supported by the National Basic Research Program of China
文摘Residual stress distributions in 7075 aluminum alloy thick plates with different thicknesses and different quenching speeds were measured. A shape function of stress distribution was proposed based on the internal stress distribution characteristics of aluminum alloy. Using nonlinear regression technology,the function between stress value of key points on internal stress curve and surface stress of the plate was obtained. Based on the measured surface stress,stress value of key points and stress distribution shape,the internal stress distribution can be reconstructed. The experiments show that the model is of good engineering practicality.
基金Project(2002CB312200) supported by the National Key Fundamental Research and Development Program of China project(60574019) supported by the National Natural Science Foundation of China
文摘Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs based MPC was derived, and then the necessary and sufficient stability condition for MPC closed loop was given according to SVM model, and finally a method of judging the discrepancy between SVM model and the actual plant was presented, and consequently the constraint sets, which can guarantee that the stability condition is still robust for model/plant mismatch within some given bounds, were obtained by applying small-gain theorem. Simulation experiments show the proposed stability condition and robust constraint sets can provide a convenient way of adjusting controller parameters to ensure a closed-loop with larger stable margin.
基金Projects(61573052,61273132)supported by the National Natural Science Foundation of China
文摘This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.
文摘To predict the erosion and abrasion of high bore pressure tank gun barrel, the least square support vector machine (LSSVM) algorithm was used. Based on the gun firing test data, the prediction model for barrel's erosion and abrasion was established. It was adopted to predict the wear increment of gun barrel. The results show that the prediction values given by the model coincide with the measured data better, and the model can predict the barrel's wear accurately and rapidly.
基金Project(201606090050)supported by China Scholarship CouncilProject(51278104)supported by the National Natural Science Foundation of China+2 种基金Project(2011Y03)supported by Jiangsu Province Transportation Scientific Research Programs,ChinaProject(20133204120015)supported by the Research Fund for the Doctoral Program of Higher Education of ChinaProject(12KJB560003)supported by Jiangsu Province Universities Natural Science Foundation,China
文摘The structural health status of Hunan Road Bridge during its two-year service period from April 2015 to April 2017 was studied based on monitored data.The Hunan Road Bridge is the widest concrete self-anchored suspension bridge in China at present.Its structural changes and safety were evaluated using the health monitoring data,which included deformations,detailed stresses,and vibration characteristics.The influences of the single and dual effects comprising the ambient temperature changes and concrete shrinkage and creep(S&C)were analyzed based on the measured data.The ANSYS beam finite element model was established and validated by the measured bridge completion state.The comparative analyses of the prediction results of long-term concrete S&C effects were conducted using CEB-FIP 90 and B3 prediction models.The age-adjusted effective modulus method was adopted to simulate the aging behavior of concrete.Prestress relaxation was considered in the stepwise calculation.The results show that the transverse deviations of the towers are noteworthy.The spatial effect of the extra-wide girder is significant,as the compressive stress variations at the girder were uneven along the transverse direction.General increase and decrease in the girder compressive stresses were caused by seasonal ambient warming and cooling,respectively.The temperature gradient effects in the main girder were significant.Comparisons with the measured data showed that more accurate prediction results were obtained with the B3 prediction model,which can consider the concrete material parameters,than with the CEB-FIP 90 model.Significant deflection of the midspan girder in the middle region will be caused by the deviations of the cable anchoring positions at the girder ends and tower tops toward the midspan due to concrete S&C.The increase in the compressive stresses at the top plate and decrease in the stresses at the bottom plate at the middle midspan will be significant.The pre-deviations of the towers toward the sidespan and pre-lift of the midspan girder can reduce the adverse influences of concrete S&C on the structural health of the self-anchored suspension bridge with extra-wide concrete girder.
基金Project(61074074)supported by the National Natural Science Foundation,ChinaProject(KT2012C01J0401)supported by the Group Innovation Fund,China
文摘An improved model predictive control algorithm is proposed for Hammerstein-Wiener nonlinear systems.The proposed synthesis algorithm contains two parts:offline design the polytopic invariant sets,and online solve the min-max optimization problem.The polytopic invariant set is adopted to replace the traditional ellipsoid invariant set.And the parameter-correlation nonlinear control law is designed to replace the traditional linear control law.Consequently,the terminal region is enlarged and the control effect is improved.Simulation and experiment are used to verify the validity of the wind tunnel flow field control algorithm.
