To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the mod...To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the model predictive static programming method is developed by incorporating neighboring terms and trust region,enabling rapid generation of precise optimal solutions.Next,an adaptive fish swarm optimization technique is employed to identify a sub-optimal solution,while a momentum gradient descent method with learning rate decay ensures the convergence to the global optimal solution.To validate the feasibility and accuracy of the proposed method,a near-space vehicle example is analyzed and simulated during its glide phase.The simulation results demonstrate that the proposed method aligns with theoretical derivations and outperforms existing methods in terms of convergence speed and accuracy.Therefore,the proposed method offers significant practical value for solving the fast trajectory optimization problem in near-space vehicle applications.展开更多
Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to en...Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.展开更多
To evaluate the heat performance of the lifting-body entry vehicle during the hypersonic gliding phase,entry flight heat tests involving the determination of the maximum peak-heat-flux entry trajectory with complex co...To evaluate the heat performance of the lifting-body entry vehicle during the hypersonic gliding phase,entry flight heat tests involving the determination of the maximum peak-heat-flux entry trajectory with complex constraints are essential.A significant obstacle is the uncertainty of passage time or energy states of the maximum peak entry heat flux point and waypoints.This paper showcases an endeavour to leverage disjunctive programming and combinatorial theory for the max-max type(maximum peak-heat-flux)Entry Trajectory Optimization(ETO)problems with complex constraints such as dynamic pressure,normal load,waypoints,and no-fly zones.The concept of a"generalized waypoint"is introduced,and the maximum peak-heat-flux point is regarded as a"generalized waypoint".Through the application of propositional calculus rules,the derivation of generalized waypoints incorporating various physical quantities and magnitudes such as heat flux density,longitude,and latitude is actualized in one disjunctive normal form,enabling resolution via a unified method.Consequently,a novel method based on combinatorial prior rules is proposed,utilizing Successive Mixed-Integer Nonlinear Programming(SMINLP)to optimize various heat entry test flight trajectories.Numerical experiments are provided to show the computational accuracy,stability,and adaptability of the proposed method in solving maxmax type entry optimal control problems.展开更多
When signal-to-interference ratio is low, the energy of strong interference leaked from the side lobe of beam pattern will infect the detection of weak target. Therefore, the beam pattern needs to be op...When signal-to-interference ratio is low, the energy of strong interference leaked from the side lobe of beam pattern will infect the detection of weak target. Therefore, the beam pattern needs to be optimized. The existing Dolph-Chebyshev weighting method can get the lowest side lobe level under given main lobe width, but for the other non-uniform circular array and nonlinear array, the low side lobe pattern needs to be designed specially. The second order cone programming optimization （SOCP） algorithm proposed in the paper transforms the optimization of the beam pattern into a standard convex optimization problem. Thus there is a paradigm to follow for any array formation, which not only achieves the purpose of Dolph-Chebyshev weighting, but also solves the problem of the increased side lobe when the signal is at end fire direction The simulation proves that the SOCP algorithm can detect the weak target better than the conventional beam forming.展开更多
Off-line programming （OLP） system becomes one of the most important programming modules for the robotic belt grinding process, however there lacks research on increasing the grinding dexterous space depending on the...Off-line programming （OLP） system becomes one of the most important programming modules for the robotic belt grinding process, however there lacks research on increasing the grinding dexterous space depending on the OLP system. A new type of grinding robot and a novel robotic belt grinding workcell are forwarded, and their features are briefly introduced. An open and object-oriented off-line programming system is developed for this robotic belt grinding system. The parameters of the trimmed surface are read from the initial graphics exchange specification （IGES） file of the CAD model of the workpiece. The deBoor-Cox basis function is used to sample the grinding target with local contact frame on the workpiece. The numerical formula of inverse kinematics is set up based on Newton＇s iterative procedure, to calculate the grinding robot configurations corresponding to the grinding targets. After the grinding path is obtained, the OLP system turns to be more effective than the teach-by-showing system. In order to improve the grinding workspace, an optimization algorithm for dynamic tool frame is proposed and performed on the special robotic belt grinding system. The initial tool frame and the interval of neighboring tool frames are defined as the preparation of the algorithm. An optimized tool local frame can be selected to grind the complex surface for a maximum dexterity index of the robot. Under the optimization algorithm, a simulation of grinding a vane is included and comparison of grinding workspace is done before and after the tool frame optimization. By the algorithm, the grinding workspace can be enlarged. Moreover the dynamic tool frame can be considered to add one degree-of-freedom to the grinding kinematical chain, which provides the theoretical support for the improvement of robotic dexterity for the complex surface grinding.展开更多
Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and ...Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and development of the army need top-down,top-level design,and comprehensive plan-ning.The traditional project development model is no longer suf-ficient to meet the army’s complex capability requirements.Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effective-ness.At the same time,when a program consists of large-scale project data,the effectiveness of the traditional,precise mathe-matical planning method is greatly reduced because it is time-consuming,costly,and impractical.To solve above problems,this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algo-rithm and verifies the effectiveness and feasibility of the model and algorithm through an example.The results show that the hybrid algorithm proposed in this paper is better than the exist-ing meta-heuristic algorithm.展开更多
Byproduct gas is an important secondary energy in iron and steel industry, and its optimization is vital to cost reduction. With the development of iron and steel industry to be more eco-friendly, it is necessary to c...Byproduct gas is an important secondary energy in iron and steel industry, and its optimization is vital to cost reduction. With the development of iron and steel industry to be more eco-friendly, it is necessary to construct an integrated optimized system, taking economics, energy consumption and environment into consideration. Therefore, the environmental cost caused by pollutants discharge should be factored in total cost when optimizing byproduct gas distribution. A green mixed integer linear programming （MILP） model for the optimization of byproduct gases was established to reduce total cost, including both operation cost and environmental cost. The operation cost included penalty for gas deviation, costs of fuel and water consumption, holder booster trip penalty, and so forth; while the environmental cost consisted of penalties for both direct and indirect pollutants discharge. Case study showed that the proposed model brought an optimum solution and 2.2% of the total cost could be reduced compared with previous one.展开更多
A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems....A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems.The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods.Experimental results indicate that in terms of robustness and final convergence speed,CLSPSO is better than other five algorithms in solving many of these problems.Furthermore,CLSPSO exhibits good performance in solving two high-dimensional problems,and it finds better solutions than the known ones.A performance index(PI) is introduced to fairly compare the above six algorithms,and the obtained values of(PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.展开更多
Soft robots, inspired by the flexibility and versatility of biological organisms, have potential in a variety of applications. Recent advancements in magneto-soft robots have demonstrated their abilities to achieve pr...Soft robots, inspired by the flexibility and versatility of biological organisms, have potential in a variety of applications. Recent advancements in magneto-soft robots have demonstrated their abilities to achieve precise remote control through magnetic fields, enabling multi-modal locomotion and complex manipulation tasks. Nonetheless, two main hurdles must be overcome to advance the field: developing a multi-component substrate with embedded magnetic particles to ensure the requisite flexibility and responsiveness, and devising a cost-effective,straightforward method to program three-dimensional distributed magnetic domains without complex processing and expensive machinery. Here, we introduce a cost-effective and simple heat-assisted in-situ integrated molding fabrication method for creating magnetically driven soft robots with three-dimensional programmable magnetic domains. By synthesizing a composite material with neodymium-iron-boron(NdFeB) particles embedded in a polydimethylsiloxane(PDMS) and Ecoflex matrix(PDMS:Ecoflex = 1:2 mass ratio, 50% magnetic particle concentration), we achieved an optimized balance of flexibility, strength, and magnetic responsiveness. The proposed heat-assisted in-situ magnetic domains programming technique,performed at an experimentally optimized temperature of 120℃, resulted in a 2 times magnetization strength(9.5 mT) compared to that at 20℃(4.8 m T), reaching a saturation level comparable to a commercial magnetizer. We demonstrated the versatility of our approach through the fabrication of six kinds of robots, including two kinds of two-dimensional patterned soft robots(2D-PSR), a circular six-pole domain distribution magnetic robot(2D-CSPDMR), a quadrupedal walking magnetic soft robot(QWMSR), an object manipulation robot(OMR), and a hollow thin-walled spherical magneto-soft robot(HTWSMSR). The proposed method provides a practical solution to create highly responsive and adaptable magneto-soft robots.展开更多
The increasing global demand for energy,coupled with concerns about environmental sustainability,has underscored the need for a transition toward renewable energy sources.A well-structured teaching program under the f...The increasing global demand for energy,coupled with concerns about environmental sustainability,has underscored the need for a transition toward renewable energy sources.A well-structured teaching program under the framework of sustainable development in renewable energy seeks to give students the information,abilities,and critical thinking needed to solve energy-related problems sustainably.This research proposes AI-powered personalized learning,innovative real-time integration of diverse data,and adaptive teaching strategies to enhance student understanding regarding renewable energy concepts.The sheep flock-optimized innovative recurrent neural network(SFO-IRNN)will recommend relevant topics and resources based on students’performance.Renewable energy teaching data from assessmethments are combined with real-time IoT-based renewable energy data.This dataset contains renewable energy education using AI-driven teaching methods and internet-based learning.The data was preprocessed by handling missing values and min-max scaling.The data features were extracted using Fourier Transform(FT).Further application of 10-fold cross-validation will increase the reliability of the model as it can evaluate its performance metrics like accuracy,F1-score,recall,and precision on different subsets of student data,which improves its robustness and prevents overfitting.The findings showed that the proposed method is significantly better,which ensures that the students have a deeper theoretical and practical understanding of renewable energy technologies.In addition,integrating real-time IoT data from renewable energy sources gives students a chance to do live simulations and problems that would enhance analytical thinking and hands-on learning.The research shows that AI provides context-aware guidance on sustainable energy infrastructure,enhancing interactive and personalized learning.展开更多
In this article,a novel model-free coordinated optimal regulation design methodology is proposed for the rigidly connected dual permanent magnet synchronous motor(PMSM)system via adaptive dynamic programming(ADP).Firs...In this article,a novel model-free coordinated optimal regulation design methodology is proposed for the rigidly connected dual permanent magnet synchronous motor(PMSM)system via adaptive dynamic programming(ADP).First,we adopt the classical master-slave structure to maintain torque synchronization by virtue of field-oriented control.Then,a reducedorder model of the dual-PMSM system is established through the application of singular perturbation theory(SPT),which is of significance to decrease the learning time and computational complexity in the outer speed loop design.Afterwards,we design a coordinated adaptive optimal regulator in framework of ADP to drive the speed of girth gear asymptotic tracking the reference signal and accommodate the load torque disturbance,which is independent of the knowledge of model parameters of the system.According to SPT,we analyze the suboptimality,closed-loop stability,and robustness properties of the obtained controller under mild conditions.Finally,comprehensive experimental studies are provided to verify that the proposed control strategy can achieve the speed regulation and the torque synchronization,as well as ameliorate the transient response.展开更多
Recent research on deterministic methods for circulating cooling water systems optimization has been well developed. However, the actual operating conditions of the system are mostly variable, so the system obtained u...Recent research on deterministic methods for circulating cooling water systems optimization has been well developed. However, the actual operating conditions of the system are mostly variable, so the system obtained under deterministic conditions may not be stable and economical. This paper studies the optimization of circulating cooling water systems under uncertain circumstance. To improve the reliability of the system and reduce the water and energy consumption, the influence of different uncertain parameters is taken into consideration. The chance constrained programming method is used to build a model under uncertain conditions, where the confidence level indicates the degree of constraint violation. Probability distribution functions are used to describe the form of uncertain parameters. The objective is to minimize the total cost and obtain the optimal cooling network configuration simultaneously.An algorithm based on Monte Carlo method is proposed, and GAMS software is used to solve the mixed integer nonlinear programming model. A case is optimized to verify the validity of the model. Compared with the deterministic optimization method, the results show that when considering the different types of uncertain parameters, a system with better economy and reliability can be obtained(total cost can be reduced at least 2%).展开更多
This paper is based on the finite and dispersed data which were obtained from the experiments of the wind tunnel and of the force measurement and from the high-speed photography. It analyses and optimizes the take-off...This paper is based on the finite and dispersed data which were obtained from the experiments of the wind tunnel and of the force measurement and from the high-speed photography. It analyses and optimizes the take-off movement of ski jumping with the theory of dynamics of systems of rigid bodies and with the method of mathematical programming. The paper describes the optimal take-off movement of ski jumping. Furthermore, it presents an example and compares the result with those of other papers published at home and abroad. The comparison shows that our computation and optimization are reasonable and well-grounded.展开更多
Optimal trajectory planning for robot manipulators plays an important role in implementing the high productivity for robots. The performance indexes used in optimal trajectory planning are classified into two main cat...Optimal trajectory planning for robot manipulators plays an important role in implementing the high productivity for robots. The performance indexes used in optimal trajectory planning are classified into two main categories: optimum traveling time and optimum mechanical energy of the actuators. The current trajectory planning algorithms are designed based on one of the above two performance indexes. So far, there have been few planning algorithms designed to satisfy two performance indexes simultaneously. On the other hand, some deficiencies arise in the existing integrated optimi2ation algorithms of trajectory planning. In order to overcome those deficiencies, the integrated optimization algorithms of trajectory planning are presented based on the complete analysis for trajectory planning of robot manipulators. In the algorithm, two object functions are designed based on the specific weight coefficient method and ' ideal point strategy. Moreover, based on the features of optimization problem, the intensified evolutionary programming is proposed to solve the corresponding optimization model. Especially, for the Stanford Robot,the high-quality solutions are found at a lower cost.展开更多
In short-term operation of natural gas network,the impact of demand uncertainty is not negligible.To address this issue we propose a two-stage robust model for power cost minimization problem in gunbarrel natural gas ...In short-term operation of natural gas network,the impact of demand uncertainty is not negligible.To address this issue we propose a two-stage robust model for power cost minimization problem in gunbarrel natural gas networks.The demands between pipelines and compressor stations are uncertain with a budget parameter,since it is unlikely that all the uncertain demands reach the maximal deviation simultaneously.During solving the two-stage robust model we encounter a bilevel problem which is challenging to solve.We formulate it as a multi-dimensional dynamic programming problem and propose approximate dynamic programming methods to accelerate the calculation.Numerical results based on real network in China show that we obtain a speed gain of 7 times faster in average without compromising optimality compared with original dynamic programming algorithm.Numerical results also verify the advantage of robust model compared with deterministic model when facing uncertainties.These findings offer short-term operation methods for gunbarrel natural gas network management to handle with uncertainties.展开更多
This paper introduces a practical solving scheme of gradetransition trajectory optimization(GTTO) problems under typical certificate-checking–updating framework. Due to complicated kinetics of polymerization,differen...This paper introduces a practical solving scheme of gradetransition trajectory optimization(GTTO) problems under typical certificate-checking–updating framework. Due to complicated kinetics of polymerization,differential/algebraic equations(DAEs) always cause great computational burden and system non-linearity usually makes GTTO non-convex bearing multiple optima. Therefore, coupled with the three-stage decomposition model, a three-section algorithm of dynamic programming(TSDP) is proposed based on the general iteration mechanism of iterative programming(IDP) and incorporated with adaptivegrid allocation scheme and heuristic modifications. The algorithm iteratively performs dynamic programming with heuristic modifications under constant calculation loads and adaptively allocates the valued computational resources to the regions that can further improve the optimality under the guidance of local error estimates. TSDP is finally compared with IDP and interior point method(IP) to verify its efficiency of computation.展开更多
The optimality criteria （OC） method and mathematical programming （MP） were combined to found the sectional optimization model of frame structures. Different methods were adopted to deal with the different constrai...The optimality criteria （OC） method and mathematical programming （MP） were combined to found the sectional optimization model of frame structures. Different methods were adopted to deal with the different constraints. The stress constraints as local constraints were approached by zero-order approximation and transformed into movable sectional lower limits with the full stress criterion. The displacement constraints as global constraints were transformed into explicit expressions with the unit virtual load method. Thus an approximate explicit model for the sectional optimization of frame structures was built with stress and displacement constraints. To improve the resolution efficiency, the dual-quadratic programming was adopted to transform the original optimization model into a dual problem according to the dual theory and solved iteratively in its dual space. A method called approximate scaling step was adopted to reduce computations and smooth the iterative process. Negative constraints were deleted to reduce the size of the optimization model. With MSC/Nastran software as structural solver and MSC/Patran software as developing platform, the sectional optimization software of frame structures was accomplished, considering stress and displacement constraints. The examples show that the efficiency and accuracy are improved.展开更多
An optimization design technique to obtain global solution for a two-stage operational amplifier(op-amp) with frequency compensation is presented.This frequency compensation technique can adjust the equivalent resista...An optimization design technique to obtain global solution for a two-stage operational amplifier(op-amp) with frequency compensation is presented.This frequency compensation technique can adjust the equivalent resistance to guarantee that the phase margin is stable even though circumstance temperature varies.Geometric programming is used to optimize the component values and transistor dimensions.It is used in this analog integrated circuit design to calculate these parameters automatically.This globally optimal amplifier obtains minimum power while other specifications are fulfilled.展开更多
Dynamic Programming (DP) algorithm is used to find the optimal trajectories under Beijing cycle for the power management of synergic electric system (SES) which is composed of battery and super capacitor. Feasible rul...Dynamic Programming (DP) algorithm is used to find the optimal trajectories under Beijing cycle for the power management of synergic electric system (SES) which is composed of battery and super capacitor. Feasible rules are derived from analyzing the optimal trajectories, and it has the highest contribution to Hybrid Electric Vehicle (HEV). The methods of how to get the best performance is also educed. Using the new Rule-based power management strat-egy adopted from the optimal results, it is easy to demonstrate the effectiveness of the new strategy in further improvement of the fuel economy by the synergic hybrid system.展开更多
Dynamic programming(DP) is an effective query optimization approach to select an appropriate join order for relational database management system(RDBMS) in multi-table joins. This method was extended and made availabl...Dynamic programming(DP) is an effective query optimization approach to select an appropriate join order for relational database management system(RDBMS) in multi-table joins. This method was extended and made available in distributed DBMS(D-DBMS). The structure of this optimal solution was firstly characterized according to the distributing status of tables and data, and then the recurrence relations between a problem and its sub-problems were recursively defined. DP in D-DBMS has the same time-complexity with that in centralized DBMS, while it has the capability to solve a much more sophisticated optimal problem of multi-table join in D-DBMS. The effectiveness of this optimal strategy has been proved by experiments.展开更多
基金supported by the National Science Foundation for Distinguished Young Scholars of China(No.52425212)National Key Research and Development Program of China(No.2021YFA0717100)National Natural Science Foundation of China(Nos.12072270,U2013206,and 52442214).
文摘To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the model predictive static programming method is developed by incorporating neighboring terms and trust region,enabling rapid generation of precise optimal solutions.Next,an adaptive fish swarm optimization technique is employed to identify a sub-optimal solution,while a momentum gradient descent method with learning rate decay ensures the convergence to the global optimal solution.To validate the feasibility and accuracy of the proposed method,a near-space vehicle example is analyzed and simulated during its glide phase.The simulation results demonstrate that the proposed method aligns with theoretical derivations and outperforms existing methods in terms of convergence speed and accuracy.Therefore,the proposed method offers significant practical value for solving the fast trajectory optimization problem in near-space vehicle applications.
基金supported by the National Natural Science Foundation of China(No.92371206)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.CX2023063).
文摘Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.
基金funded by the Key Laboratory of Cross-Domain Flight Interdisciplinary Technology,China(No.2024-KF02201)the National Natural Science Foundation of China(No.61973326)。
文摘To evaluate the heat performance of the lifting-body entry vehicle during the hypersonic gliding phase,entry flight heat tests involving the determination of the maximum peak-heat-flux entry trajectory with complex constraints are essential.A significant obstacle is the uncertainty of passage time or energy states of the maximum peak entry heat flux point and waypoints.This paper showcases an endeavour to leverage disjunctive programming and combinatorial theory for the max-max type(maximum peak-heat-flux)Entry Trajectory Optimization(ETO)problems with complex constraints such as dynamic pressure,normal load,waypoints,and no-fly zones.The concept of a"generalized waypoint"is introduced,and the maximum peak-heat-flux point is regarded as a"generalized waypoint".Through the application of propositional calculus rules,the derivation of generalized waypoints incorporating various physical quantities and magnitudes such as heat flux density,longitude,and latitude is actualized in one disjunctive normal form,enabling resolution via a unified method.Consequently,a novel method based on combinatorial prior rules is proposed,utilizing Successive Mixed-Integer Nonlinear Programming(SMINLP)to optimize various heat entry test flight trajectories.Numerical experiments are provided to show the computational accuracy,stability,and adaptability of the proposed method in solving maxmax type entry optimal control problems.
基金Special Item of National Major Scientific Apparatus Development(No.2013YQ140431)
文摘When signal-to-interference ratio is low, the energy of strong interference leaked from the side lobe of beam pattern will infect the detection of weak target. Therefore, the beam pattern needs to be optimized. The existing Dolph-Chebyshev weighting method can get the lowest side lobe level under given main lobe width, but for the other non-uniform circular array and nonlinear array, the low side lobe pattern needs to be designed specially. The second order cone programming optimization （SOCP） algorithm proposed in the paper transforms the optimization of the beam pattern into a standard convex optimization problem. Thus there is a paradigm to follow for any array formation, which not only achieves the purpose of Dolph-Chebyshev weighting, but also solves the problem of the increased side lobe when the signal is at end fire direction The simulation proves that the SOCP algorithm can detect the weak target better than the conventional beam forming.
基金supported by National Hi-tech Research and Development Program of China (863 Program, Grant No. 2007AA04Z2443)State Key Laboratory for Man ufacturing Systems Engineering of Xi’an Jiaotong University of China
文摘Off-line programming （OLP） system becomes one of the most important programming modules for the robotic belt grinding process, however there lacks research on increasing the grinding dexterous space depending on the OLP system. A new type of grinding robot and a novel robotic belt grinding workcell are forwarded, and their features are briefly introduced. An open and object-oriented off-line programming system is developed for this robotic belt grinding system. The parameters of the trimmed surface are read from the initial graphics exchange specification （IGES） file of the CAD model of the workpiece. The deBoor-Cox basis function is used to sample the grinding target with local contact frame on the workpiece. The numerical formula of inverse kinematics is set up based on Newton＇s iterative procedure, to calculate the grinding robot configurations corresponding to the grinding targets. After the grinding path is obtained, the OLP system turns to be more effective than the teach-by-showing system. In order to improve the grinding workspace, an optimization algorithm for dynamic tool frame is proposed and performed on the special robotic belt grinding system. The initial tool frame and the interval of neighboring tool frames are defined as the preparation of the algorithm. An optimized tool local frame can be selected to grind the complex surface for a maximum dexterity index of the robot. Under the optimization algorithm, a simulation of grinding a vane is included and comparison of grinding workspace is done before and after the tool frame optimization. By the algorithm, the grinding workspace can be enlarged. Moreover the dynamic tool frame can be considered to add one degree-of-freedom to the grinding kinematical chain, which provides the theoretical support for the improvement of robotic dexterity for the complex surface grinding.
基金supported by the National Natural Science Foundation of China(724701189072431011).
文摘Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and development of the army need top-down,top-level design,and comprehensive plan-ning.The traditional project development model is no longer suf-ficient to meet the army’s complex capability requirements.Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effective-ness.At the same time,when a program consists of large-scale project data,the effectiveness of the traditional,precise mathe-matical planning method is greatly reduced because it is time-consuming,costly,and impractical.To solve above problems,this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algo-rithm and verifies the effectiveness and feasibility of the model and algorithm through an example.The results show that the hybrid algorithm proposed in this paper is better than the exist-ing meta-heuristic algorithm.
基金Sponsored by Beijing Social Science Foundation of China(14JGC110)Social Science Research Common Program of Beijing Municipal Commission of Education of China(SM201510038011)CUEB Foundation of China(2014XJG005)
文摘Byproduct gas is an important secondary energy in iron and steel industry, and its optimization is vital to cost reduction. With the development of iron and steel industry to be more eco-friendly, it is necessary to construct an integrated optimized system, taking economics, energy consumption and environment into consideration. Therefore, the environmental cost caused by pollutants discharge should be factored in total cost when optimizing byproduct gas distribution. A green mixed integer linear programming （MILP） model for the optimization of byproduct gases was established to reduce total cost, including both operation cost and environmental cost. The operation cost included penalty for gas deviation, costs of fuel and water consumption, holder booster trip penalty, and so forth; while the environmental cost consisted of penalties for both direct and indirect pollutants discharge. Case study showed that the proposed model brought an optimum solution and 2.2% of the total cost could be reduced compared with previous one.
基金Projects(50275150,61173052) supported by the National Natural Science Foundation of ChinaProject(14FJ3112) supported by the Planned Science and Technology of Hunan Province,ChinaProject(14B033) supported by Scientific Research Fund Education Department of Hunan Province,China
文摘A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems.The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods.Experimental results indicate that in terms of robustness and final convergence speed,CLSPSO is better than other five algorithms in solving many of these problems.Furthermore,CLSPSO exhibits good performance in solving two high-dimensional problems,and it finds better solutions than the known ones.A performance index(PI) is introduced to fairly compare the above six algorithms,and the obtained values of(PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.
基金supported by National Natural Science Foundation of China(Grant Nos.62473277,62473275,62133004,52105072,and 62073230)Jiangsu Provincial Outstanding Youth Program(Grant No.BK20230072)+5 种基金National Key R&D Program of China(Grant Nos.2022YFC3802302 and 2023YFB4705600)Suzhou Industrial Foresight and Key Core Technology Project(Grant No.SYC2022044)Zhejiang Provincial Natural Science Foundation of China(Grant No.LZ24E050004)Shenzhen Polytechnic High-level Talent Start-up Project(Grant No.6023330006K)Shenzhen Science and Technology Program(Grant No.JCYJ20210324132810026)a Grant from Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems,Grants from Jiangsu QingLan Project and Jiangsu 333 high-level talents.
文摘Soft robots, inspired by the flexibility and versatility of biological organisms, have potential in a variety of applications. Recent advancements in magneto-soft robots have demonstrated their abilities to achieve precise remote control through magnetic fields, enabling multi-modal locomotion and complex manipulation tasks. Nonetheless, two main hurdles must be overcome to advance the field: developing a multi-component substrate with embedded magnetic particles to ensure the requisite flexibility and responsiveness, and devising a cost-effective,straightforward method to program three-dimensional distributed magnetic domains without complex processing and expensive machinery. Here, we introduce a cost-effective and simple heat-assisted in-situ integrated molding fabrication method for creating magnetically driven soft robots with three-dimensional programmable magnetic domains. By synthesizing a composite material with neodymium-iron-boron(NdFeB) particles embedded in a polydimethylsiloxane(PDMS) and Ecoflex matrix(PDMS:Ecoflex = 1:2 mass ratio, 50% magnetic particle concentration), we achieved an optimized balance of flexibility, strength, and magnetic responsiveness. The proposed heat-assisted in-situ magnetic domains programming technique,performed at an experimentally optimized temperature of 120℃, resulted in a 2 times magnetization strength(9.5 mT) compared to that at 20℃(4.8 m T), reaching a saturation level comparable to a commercial magnetizer. We demonstrated the versatility of our approach through the fabrication of six kinds of robots, including two kinds of two-dimensional patterned soft robots(2D-PSR), a circular six-pole domain distribution magnetic robot(2D-CSPDMR), a quadrupedal walking magnetic soft robot(QWMSR), an object manipulation robot(OMR), and a hollow thin-walled spherical magneto-soft robot(HTWSMSR). The proposed method provides a practical solution to create highly responsive and adaptable magneto-soft robots.
文摘The increasing global demand for energy,coupled with concerns about environmental sustainability,has underscored the need for a transition toward renewable energy sources.A well-structured teaching program under the framework of sustainable development in renewable energy seeks to give students the information,abilities,and critical thinking needed to solve energy-related problems sustainably.This research proposes AI-powered personalized learning,innovative real-time integration of diverse data,and adaptive teaching strategies to enhance student understanding regarding renewable energy concepts.The sheep flock-optimized innovative recurrent neural network(SFO-IRNN)will recommend relevant topics and resources based on students’performance.Renewable energy teaching data from assessmethments are combined with real-time IoT-based renewable energy data.This dataset contains renewable energy education using AI-driven teaching methods and internet-based learning.The data was preprocessed by handling missing values and min-max scaling.The data features were extracted using Fourier Transform(FT).Further application of 10-fold cross-validation will increase the reliability of the model as it can evaluate its performance metrics like accuracy,F1-score,recall,and precision on different subsets of student data,which improves its robustness and prevents overfitting.The findings showed that the proposed method is significantly better,which ensures that the students have a deeper theoretical and practical understanding of renewable energy technologies.In addition,integrating real-time IoT data from renewable energy sources gives students a chance to do live simulations and problems that would enhance analytical thinking and hands-on learning.The research shows that AI provides context-aware guidance on sustainable energy infrastructure,enhancing interactive and personalized learning.
基金supported by the National Natural Science Foundation of China(62073327,62403467,62373090,62273350,62521001)the Natural Science Foundation of Jiangsu Province(BK20241635)+2 种基金the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(CPSF)(GZB20240827)Jiangsu Funding Program for Excellent Postdoctoral Talent(2024ZB604)the China Postdoctoral Science Foundation(2024M763545,2025T054ZGMK).
文摘In this article,a novel model-free coordinated optimal regulation design methodology is proposed for the rigidly connected dual permanent magnet synchronous motor(PMSM)system via adaptive dynamic programming(ADP).First,we adopt the classical master-slave structure to maintain torque synchronization by virtue of field-oriented control.Then,a reducedorder model of the dual-PMSM system is established through the application of singular perturbation theory(SPT),which is of significance to decrease the learning time and computational complexity in the outer speed loop design.Afterwards,we design a coordinated adaptive optimal regulator in framework of ADP to drive the speed of girth gear asymptotic tracking the reference signal and accommodate the load torque disturbance,which is independent of the knowledge of model parameters of the system.According to SPT,we analyze the suboptimality,closed-loop stability,and robustness properties of the obtained controller under mild conditions.Finally,comprehensive experimental studies are provided to verify that the proposed control strategy can achieve the speed regulation and the torque synchronization,as well as ameliorate the transient response.
基金Financial support from the National Natural Science Foundation of China (22022816, 22078358)。
文摘Recent research on deterministic methods for circulating cooling water systems optimization has been well developed. However, the actual operating conditions of the system are mostly variable, so the system obtained under deterministic conditions may not be stable and economical. This paper studies the optimization of circulating cooling water systems under uncertain circumstance. To improve the reliability of the system and reduce the water and energy consumption, the influence of different uncertain parameters is taken into consideration. The chance constrained programming method is used to build a model under uncertain conditions, where the confidence level indicates the degree of constraint violation. Probability distribution functions are used to describe the form of uncertain parameters. The objective is to minimize the total cost and obtain the optimal cooling network configuration simultaneously.An algorithm based on Monte Carlo method is proposed, and GAMS software is used to solve the mixed integer nonlinear programming model. A case is optimized to verify the validity of the model. Compared with the deterministic optimization method, the results show that when considering the different types of uncertain parameters, a system with better economy and reliability can be obtained(total cost can be reduced at least 2%).
基金Project supported by the National Natutal Science Foundation of China
文摘This paper is based on the finite and dispersed data which were obtained from the experiments of the wind tunnel and of the force measurement and from the high-speed photography. It analyses and optimizes the take-off movement of ski jumping with the theory of dynamics of systems of rigid bodies and with the method of mathematical programming. The paper describes the optimal take-off movement of ski jumping. Furthermore, it presents an example and compares the result with those of other papers published at home and abroad. The comparison shows that our computation and optimization are reasonable and well-grounded.
基金This work was supported in part by National Natural Science Foundation of China (No. 69975003) and Foundation for Dissertation of Ph. D. Candidate of Central South University (No.030618) .
文摘Optimal trajectory planning for robot manipulators plays an important role in implementing the high productivity for robots. The performance indexes used in optimal trajectory planning are classified into two main categories: optimum traveling time and optimum mechanical energy of the actuators. The current trajectory planning algorithms are designed based on one of the above two performance indexes. So far, there have been few planning algorithms designed to satisfy two performance indexes simultaneously. On the other hand, some deficiencies arise in the existing integrated optimi2ation algorithms of trajectory planning. In order to overcome those deficiencies, the integrated optimization algorithms of trajectory planning are presented based on the complete analysis for trajectory planning of robot manipulators. In the algorithm, two object functions are designed based on the specific weight coefficient method and ' ideal point strategy. Moreover, based on the features of optimization problem, the intensified evolutionary programming is proposed to solve the corresponding optimization model. Especially, for the Stanford Robot,the high-quality solutions are found at a lower cost.
基金partially supported by the National Science Foundation of China(Grants 71822105 and 91746210)。
文摘In short-term operation of natural gas network,the impact of demand uncertainty is not negligible.To address this issue we propose a two-stage robust model for power cost minimization problem in gunbarrel natural gas networks.The demands between pipelines and compressor stations are uncertain with a budget parameter,since it is unlikely that all the uncertain demands reach the maximal deviation simultaneously.During solving the two-stage robust model we encounter a bilevel problem which is challenging to solve.We formulate it as a multi-dimensional dynamic programming problem and propose approximate dynamic programming methods to accelerate the calculation.Numerical results based on real network in China show that we obtain a speed gain of 7 times faster in average without compromising optimality compared with original dynamic programming algorithm.Numerical results also verify the advantage of robust model compared with deterministic model when facing uncertainties.These findings offer short-term operation methods for gunbarrel natural gas network management to handle with uncertainties.
基金Supported by the National Basic Research Program of China(2012CB720500)the National High Technology Research and Development Program of China(2013AA040702)
文摘This paper introduces a practical solving scheme of gradetransition trajectory optimization(GTTO) problems under typical certificate-checking–updating framework. Due to complicated kinetics of polymerization,differential/algebraic equations(DAEs) always cause great computational burden and system non-linearity usually makes GTTO non-convex bearing multiple optima. Therefore, coupled with the three-stage decomposition model, a three-section algorithm of dynamic programming(TSDP) is proposed based on the general iteration mechanism of iterative programming(IDP) and incorporated with adaptivegrid allocation scheme and heuristic modifications. The algorithm iteratively performs dynamic programming with heuristic modifications under constant calculation loads and adaptively allocates the valued computational resources to the regions that can further improve the optimality under the guidance of local error estimates. TSDP is finally compared with IDP and interior point method(IP) to verify its efficiency of computation.
基金Project supported by the National Natural Science Foundation of China(No. 10472003) the Natural Science Foundation of Beijing(No.3002002) the Science Foundation of Beijing Municipal Commission of Education(No.KM200410005019)
文摘The optimality criteria （OC） method and mathematical programming （MP） were combined to found the sectional optimization model of frame structures. Different methods were adopted to deal with the different constraints. The stress constraints as local constraints were approached by zero-order approximation and transformed into movable sectional lower limits with the full stress criterion. The displacement constraints as global constraints were transformed into explicit expressions with the unit virtual load method. Thus an approximate explicit model for the sectional optimization of frame structures was built with stress and displacement constraints. To improve the resolution efficiency, the dual-quadratic programming was adopted to transform the original optimization model into a dual problem according to the dual theory and solved iteratively in its dual space. A method called approximate scaling step was adopted to reduce computations and smooth the iterative process. Negative constraints were deleted to reduce the size of the optimization model. With MSC/Nastran software as structural solver and MSC/Patran software as developing platform, the sectional optimization software of frame structures was accomplished, considering stress and displacement constraints. The examples show that the efficiency and accuracy are improved.
基金the Shanghai Application Material(AM) Research Foundation (No.08700740700)
文摘An optimization design technique to obtain global solution for a two-stage operational amplifier(op-amp) with frequency compensation is presented.This frequency compensation technique can adjust the equivalent resistance to guarantee that the phase margin is stable even though circumstance temperature varies.Geometric programming is used to optimize the component values and transistor dimensions.It is used in this analog integrated circuit design to calculate these parameters automatically.This globally optimal amplifier obtains minimum power while other specifications are fulfilled.
文摘Dynamic Programming (DP) algorithm is used to find the optimal trajectories under Beijing cycle for the power management of synergic electric system (SES) which is composed of battery and super capacitor. Feasible rules are derived from analyzing the optimal trajectories, and it has the highest contribution to Hybrid Electric Vehicle (HEV). The methods of how to get the best performance is also educed. Using the new Rule-based power management strat-egy adopted from the optimal results, it is easy to demonstrate the effectiveness of the new strategy in further improvement of the fuel economy by the synergic hybrid system.
文摘Dynamic programming(DP) is an effective query optimization approach to select an appropriate join order for relational database management system(RDBMS) in multi-table joins. This method was extended and made available in distributed DBMS(D-DBMS). The structure of this optimal solution was firstly characterized according to the distributing status of tables and data, and then the recurrence relations between a problem and its sub-problems were recursively defined. DP in D-DBMS has the same time-complexity with that in centralized DBMS, while it has the capability to solve a much more sophisticated optimal problem of multi-table join in D-DBMS. The effectiveness of this optimal strategy has been proved by experiments.
