Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequent...Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.展开更多
An improved approach is presented in this paper to implement highly constrained cooperative guidance to attack a stationary target.The problem with time-varying Proportional Navigation(PN)gain is first formulated as a...An improved approach is presented in this paper to implement highly constrained cooperative guidance to attack a stationary target.The problem with time-varying Proportional Navigation(PN)gain is first formulated as a nonlinear optimal control problem,which is difficult to solve due to the existence of nonlinear kinematics and nonconvex constraints.After convexification treatments and discretization,the solution to the original problem can be approximately obtained by solving a sequence of Second-Order Cone Programming(SOCP)problems,which can be readily solved by state-of-the-art Interior-Point Methods(IPMs).To mitigate the sensibility of the algorithm on the user-provided initial profile,a Two-Stage Sequential Convex Programming(TSSCP)method is presented in detail.Furthermore,numerical simulations under different mission scenarios are conducted to show the superiority of the proposed method in solving the cooperative guidance problem.The research indicated that the TSSCP method is more tractable and reliable than the traditional methods and has great potential for real-time processing and on-board implementation.展开更多
The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense ...The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense anti-missile targets defense problem is abstracted as a nonconvex constrained combinatorial optimization problem with the optimization objective of maximizing the degree of contribution of the processing scheme to non-cooperative targets, and the constraints mainly consider geographical conditions and anti-missile equipment resources. The grid discretization concept is used to partition the defense area into network nodes, and the overall defense strategy scheme is described as a nonlinear programming problem to solve the minimum defense cost within the maximum defense capability of the defense system network. In the solution of the minimum defense cost problem, the processing scheme, equipment coverage capability, constraints and node cost requirements are characterized, then a nonlinear mathematical model of the non-cooperative target distributed hybrid processing optimization problem is established, and a local optimal solution based on the sequential quadratic programming algorithm is constructed, and the optimal firepower processing scheme is given by using the sequential quadratic programming method containing non-convex quadratic equations and inequality constraints. Finally, the effectiveness of the proposed method is verified by simulation examples.展开更多
In order to slove the large-scale nonlinear programming （NLP） problems efficiently, an efficient optimization algorithm based on reduced sequential quadratic programming （rSQP） and automatic differentiation （AD）...In order to slove the large-scale nonlinear programming （NLP） problems efficiently, an efficient optimization algorithm based on reduced sequential quadratic programming （rSQP） and automatic differentiation （AD） is presented in this paper. With the characteristics of sparseness, relatively low degrees of freedom and equality constraints utilized, the nonlinear programming problem is solved by improved rSQP solver. In the solving process, AD technology is used to obtain accurate gradient information. The numerical results show that the combined algorithm, which is suitable for large-scale process optimization problems, can calculate more efficiently than rSQP itself.展开更多
A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which i...A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and linear approximations to constraints is solved to get a search direction for a merit function. The merit function is formulated by augmenting the Lagrangian function with a penalty term. A line search is carried out along the search direction to determine a step length such that the merit function is decreased. The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadratic programming methods.展开更多
The program construction process is based on rigorous mathematical reasoning,which leads to a fully correct algorithmic program via step-by-step refinement of the program specifications.The existing program constructi...The program construction process is based on rigorous mathematical reasoning,which leads to a fully correct algorithmic program via step-by-step refinement of the program specifications.The existing program construction methods'refinement process is partly based on individual subjective speculation and analysis,which lacks a precise guidance method.Meanwhile,efficiency factors have usually been ignored in the construction process,and most of the constructed abstract programs cannot be run directly by machines.In order to solve these problems,a novel program construction method for the sequence statistical class algorithms based on bidirectional scan induction is proposed in this paper.The method takes into account the efficiency factor and thus improves the Morgan's refinement calculus.Furthermore,this paper validates the method's feasibility using an efficiency-sensitive sequential statistics class algorithm as a program construction example.The method proposed in this paper realizes the correctness construction process from program specifications to efficient executable programs.展开更多
In this paper our studies about the sequential testing program for predicting and identificating carcinogens, sequential discriminant method and cost- effectiveness analysis are summarized. The analysis of our databas...In this paper our studies about the sequential testing program for predicting and identificating carcinogens, sequential discriminant method and cost- effectiveness analysis are summarized. The analysis of our database of carcinogeniclty and genotoxicity of chemicals demonstrates the uncertainty . of short- term tests ( STTs ) to predict carcinogens and the results of most routine STTs are statistically dependent. We recommend the sequential testing program combining STTs and carclnogenicity assay, the optimal STT batteries, the rules of the sequential discrimination and the preferal choices of STTs tor specific chemical class. For illustrative pmposes the carclnogenicity prediction of several sample chamicals is presented. The results of cost-effectiveness analysis suggest that this program has vast social-economic effectiveness.展开更多
This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures.These structures,commonly encounter...This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures.These structures,commonly encountered in engineering applications,often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design variables.As a result,sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization challenges.Over the past several decades,topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical backgrounds.In comparison to the large-scale equation solution,sensitivity analysis,graphics post-processing,etc.,the progress of the sequential approximation functions and their corresponding optimizersmake sluggish progress.Researchers,particularly novices,pay special attention to their difficulties with a particular problem.Thus,this paper provides an overview of sequential approximation functions,related literature on topology optimization methods,and their applications.Starting from optimality criteria and sequential linear programming,the other sequential approximate optimizations are introduced by employing Taylor expansion and intervening variables.In addition,recent advancements have led to the emergence of approaches such as Augmented Lagrange,sequential approximate integer,and non-gradient approximation are also introduced.By highlighting real-world applications and case studies,the paper not only demonstrates the practical relevance of these methods but also underscores the need for continued exploration in this area.Furthermore,to provide a comprehensive overview,this paper offers several novel developments that aim to illuminate potential directions for future research.展开更多
The Kuhn-Tucker theorem in nondifferential form is a well-known classical optimality criterion for a convex programming problems which is true for a convex problem in the case when a Kuhn-Tucker vector exists. It is n...The Kuhn-Tucker theorem in nondifferential form is a well-known classical optimality criterion for a convex programming problems which is true for a convex problem in the case when a Kuhn-Tucker vector exists. It is natural to extract two features connected with the classical theorem. The first of them consists in its possible “impracticability” (the Kuhn-Tucker vector does not exist). The second feature is connected with possible “instability” of the classical theorem with respect to the errors in the initial data. The article deals with the so-called regularized Kuhn-Tucker theorem in nondifferential sequential form which contains its classical analogue. A proof of the regularized theorem is based on the dual regularization method. This theorem is an assertion without regularity assumptions in terms of minimizing sequences about possibility of approximation of the solution of the convex programming problem by minimizers of its regular Lagrangian, that are constructively generated by means of the dual regularization method. The major distinctive property of the regularized Kuhn-Tucker theorem consists that it is free from two lacks of its classical analogue specified above. The last circumstance opens possibilities of its application for solving various ill-posed problems of optimization, optimal control, inverse problems.展开更多
With the rapid changes of the flight environment and situation,there will be various unexpected situations while multiple missiles are performing the missions.To fast cope with the various situations in mission execut...With the rapid changes of the flight environment and situation,there will be various unexpected situations while multiple missiles are performing the missions.To fast cope with the various situations in mission executions,the conventional sequential convex programming algorithm and the parallel-based sequential convex programming algorithm for multiple missiles fast trajectory replanning are proposed in this paper.The originally non-convex trajectory optimization problem is reformulated into a series of convex optimization subproblems based on the sequential convex programming method.The conventional sequential convex programming algorithm is developed through linearization,successive convexification,and relaxation techniques to solve the convex optimization subproblems iteratively.However,multiple missiles are related through various cooperative constraints.When the trajectory optimization of multiple missiles is formulated as an optimal control problem to solve,the complexity of the problem will increase dramatically as the number of missiles increases.To alleviate the coupled effect caused by multiple aerodynamically controlled missiles,the parallel-based sequential convex programming algorithm is proposed to solve the trajectory optimization problem for multiple missiles in parallel,reducing the complexity of the trajectory optimization problem and significantly shortening the computation time.Numerical simulations are provided to verify the convergence and effectiveness of the conventional sequential convex programming algorithm and the parallel-based sequential convex programming algorithm to cope with the trajectory optimization problem with various constraints.Furthermore,the optimality and the real-time performance of the proposed algorithms are discussed in comparative simulation examples.展开更多
In this paper,a computation framework for addressing combined economic and emission dispatch(CEED)problem with valve-point effects as well as stochastic wind power considering unit commitment(UC)using a hybrid approac...In this paper,a computation framework for addressing combined economic and emission dispatch(CEED)problem with valve-point effects as well as stochastic wind power considering unit commitment(UC)using a hybrid approach connecting sequential quadratic programming(SQP)and particle swarm optimization(PSO)is proposed.The CEED problem aims to minimize the scheduling cost and greenhouse gases(GHGs)emission cost.Here the GHGs include carbon dioxide(CO_(2)),nitrogen dioxide(NO_(2)),and sulphur oxides(SO_(x)).A dispatch model including both thermal generators and wind farms is developed.The probability of stochastic wind power based on the Weibull distribution is included in the CEED model.The model is tested on a standard system involving six thermal units and two wind farms.A set of numerical case studies are reported.The performance of the hybrid computational method is validated by comparing with other solvers on the test system.展开更多
In this paper, we propose a new hybrid method called SQPBSA which combines backtracking search optimization algorithm （BSA） and sequential quadratic programming （SQP）. BSA, as an exploration search engine, gives a...In this paper, we propose a new hybrid method called SQPBSA which combines backtracking search optimization algorithm （BSA） and sequential quadratic programming （SQP）. BSA, as an exploration search engine, gives a good direction to the global optimal region, while SQP is used as a local search technique to exploit the optimal solution. The experiments are carried on two suits of 28 functions proposed in the CEC-2013 competitions to verify the performance of SQPBSA. The results indicate the proposed method is effective and competitive.展开更多
基金supported by the National Natural Science Foundation of China(No.62203256)。
文摘Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.
基金supported by the Joint Foundation of the Ministry of Education of China(No.6141A02022340).
文摘An improved approach is presented in this paper to implement highly constrained cooperative guidance to attack a stationary target.The problem with time-varying Proportional Navigation(PN)gain is first formulated as a nonlinear optimal control problem,which is difficult to solve due to the existence of nonlinear kinematics and nonconvex constraints.After convexification treatments and discretization,the solution to the original problem can be approximately obtained by solving a sequence of Second-Order Cone Programming(SOCP)problems,which can be readily solved by state-of-the-art Interior-Point Methods(IPMs).To mitigate the sensibility of the algorithm on the user-provided initial profile,a Two-Stage Sequential Convex Programming(TSSCP)method is presented in detail.Furthermore,numerical simulations under different mission scenarios are conducted to show the superiority of the proposed method in solving the cooperative guidance problem.The research indicated that the TSSCP method is more tractable and reliable than the traditional methods and has great potential for real-time processing and on-board implementation.
基金supported by the National Natural Science Foundation of China (61903025)the Fundamental Research Funds for the Cent ral Universities (FRF-IDRY-20-013)。
文摘The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense anti-missile targets defense problem is abstracted as a nonconvex constrained combinatorial optimization problem with the optimization objective of maximizing the degree of contribution of the processing scheme to non-cooperative targets, and the constraints mainly consider geographical conditions and anti-missile equipment resources. The grid discretization concept is used to partition the defense area into network nodes, and the overall defense strategy scheme is described as a nonlinear programming problem to solve the minimum defense cost within the maximum defense capability of the defense system network. In the solution of the minimum defense cost problem, the processing scheme, equipment coverage capability, constraints and node cost requirements are characterized, then a nonlinear mathematical model of the non-cooperative target distributed hybrid processing optimization problem is established, and a local optimal solution based on the sequential quadratic programming algorithm is constructed, and the optimal firepower processing scheme is given by using the sequential quadratic programming method containing non-convex quadratic equations and inequality constraints. Finally, the effectiveness of the proposed method is verified by simulation examples.
文摘In order to slove the large-scale nonlinear programming （NLP） problems efficiently, an efficient optimization algorithm based on reduced sequential quadratic programming （rSQP） and automatic differentiation （AD） is presented in this paper. With the characteristics of sparseness, relatively low degrees of freedom and equality constraints utilized, the nonlinear programming problem is solved by improved rSQP solver. In the solving process, AD technology is used to obtain accurate gradient information. The numerical results show that the combined algorithm, which is suitable for large-scale process optimization problems, can calculate more efficiently than rSQP itself.
文摘A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and linear approximations to constraints is solved to get a search direction for a merit function. The merit function is formulated by augmenting the Lagrangian function with a penalty term. A line search is carried out along the search direction to determine a step length such that the merit function is decreased. The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadratic programming methods.
基金Supported by the National Natural Science Foundation of China(62262031)the Jiangxi Provincial Natural Science Foundation(20232BAB202010)+1 种基金the Science and Technology Project of Education Department of Jiangxi Province(GJJ210307,GJJ2200302)the Cultivation Project for Academic and Technical Leader in Major Disciplines in Jiangxi Province(20232BCJ22013)。
文摘The program construction process is based on rigorous mathematical reasoning,which leads to a fully correct algorithmic program via step-by-step refinement of the program specifications.The existing program construction methods'refinement process is partly based on individual subjective speculation and analysis,which lacks a precise guidance method.Meanwhile,efficiency factors have usually been ignored in the construction process,and most of the constructed abstract programs cannot be run directly by machines.In order to solve these problems,a novel program construction method for the sequence statistical class algorithms based on bidirectional scan induction is proposed in this paper.The method takes into account the efficiency factor and thus improves the Morgan's refinement calculus.Furthermore,this paper validates the method's feasibility using an efficiency-sensitive sequential statistics class algorithm as a program construction example.The method proposed in this paper realizes the correctness construction process from program specifications to efficient executable programs.
文摘In this paper our studies about the sequential testing program for predicting and identificating carcinogens, sequential discriminant method and cost- effectiveness analysis are summarized. The analysis of our database of carcinogeniclty and genotoxicity of chemicals demonstrates the uncertainty . of short- term tests ( STTs ) to predict carcinogens and the results of most routine STTs are statistically dependent. We recommend the sequential testing program combining STTs and carclnogenicity assay, the optimal STT batteries, the rules of the sequential discrimination and the preferal choices of STTs tor specific chemical class. For illustrative pmposes the carclnogenicity prediction of several sample chamicals is presented. The results of cost-effectiveness analysis suggest that this program has vast social-economic effectiveness.
基金financially supported by the National Key R&D Program (2022YFB4201302)Guang Dong Basic and Applied Basic Research Foundation (2022A1515240057)the Huaneng Technology Funds (HNKJ20-H88).
文摘This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures.These structures,commonly encountered in engineering applications,often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design variables.As a result,sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization challenges.Over the past several decades,topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical backgrounds.In comparison to the large-scale equation solution,sensitivity analysis,graphics post-processing,etc.,the progress of the sequential approximation functions and their corresponding optimizersmake sluggish progress.Researchers,particularly novices,pay special attention to their difficulties with a particular problem.Thus,this paper provides an overview of sequential approximation functions,related literature on topology optimization methods,and their applications.Starting from optimality criteria and sequential linear programming,the other sequential approximate optimizations are introduced by employing Taylor expansion and intervening variables.In addition,recent advancements have led to the emergence of approaches such as Augmented Lagrange,sequential approximate integer,and non-gradient approximation are also introduced.By highlighting real-world applications and case studies,the paper not only demonstrates the practical relevance of these methods but also underscores the need for continued exploration in this area.Furthermore,to provide a comprehensive overview,this paper offers several novel developments that aim to illuminate potential directions for future research.
文摘The Kuhn-Tucker theorem in nondifferential form is a well-known classical optimality criterion for a convex programming problems which is true for a convex problem in the case when a Kuhn-Tucker vector exists. It is natural to extract two features connected with the classical theorem. The first of them consists in its possible “impracticability” (the Kuhn-Tucker vector does not exist). The second feature is connected with possible “instability” of the classical theorem with respect to the errors in the initial data. The article deals with the so-called regularized Kuhn-Tucker theorem in nondifferential sequential form which contains its classical analogue. A proof of the regularized theorem is based on the dual regularization method. This theorem is an assertion without regularity assumptions in terms of minimizing sequences about possibility of approximation of the solution of the convex programming problem by minimizers of its regular Lagrangian, that are constructively generated by means of the dual regularization method. The major distinctive property of the regularized Kuhn-Tucker theorem consists that it is free from two lacks of its classical analogue specified above. The last circumstance opens possibilities of its application for solving various ill-posed problems of optimization, optimal control, inverse problems.
基金supported by the National Natural Science Foundation of China(Grant No.12372044).
文摘With the rapid changes of the flight environment and situation,there will be various unexpected situations while multiple missiles are performing the missions.To fast cope with the various situations in mission executions,the conventional sequential convex programming algorithm and the parallel-based sequential convex programming algorithm for multiple missiles fast trajectory replanning are proposed in this paper.The originally non-convex trajectory optimization problem is reformulated into a series of convex optimization subproblems based on the sequential convex programming method.The conventional sequential convex programming algorithm is developed through linearization,successive convexification,and relaxation techniques to solve the convex optimization subproblems iteratively.However,multiple missiles are related through various cooperative constraints.When the trajectory optimization of multiple missiles is formulated as an optimal control problem to solve,the complexity of the problem will increase dramatically as the number of missiles increases.To alleviate the coupled effect caused by multiple aerodynamically controlled missiles,the parallel-based sequential convex programming algorithm is proposed to solve the trajectory optimization problem for multiple missiles in parallel,reducing the complexity of the trajectory optimization problem and significantly shortening the computation time.Numerical simulations are provided to verify the convergence and effectiveness of the conventional sequential convex programming algorithm and the parallel-based sequential convex programming algorithm to cope with the trajectory optimization problem with various constraints.Furthermore,the optimality and the real-time performance of the proposed algorithms are discussed in comparative simulation examples.
文摘In this paper,a computation framework for addressing combined economic and emission dispatch(CEED)problem with valve-point effects as well as stochastic wind power considering unit commitment(UC)using a hybrid approach connecting sequential quadratic programming(SQP)and particle swarm optimization(PSO)is proposed.The CEED problem aims to minimize the scheduling cost and greenhouse gases(GHGs)emission cost.Here the GHGs include carbon dioxide(CO_(2)),nitrogen dioxide(NO_(2)),and sulphur oxides(SO_(x)).A dispatch model including both thermal generators and wind farms is developed.The probability of stochastic wind power based on the Weibull distribution is included in the CEED model.The model is tested on a standard system involving six thermal units and two wind farms.A set of numerical case studies are reported.The performance of the hybrid computational method is validated by comparing with other solvers on the test system.
基金Acknowledgements This work was supported by the NSFC-Guangdong Joint Fund （U1201258）, the National Natural Science Foundation of China （Grant No. 61573219）, the Shandong Natural Science Funds for Distinguished Young Scholars （JQ201316）, the Fundamental Research Funds of Shandong University （2014JC028）, and the Natural Science Foundation of Fujian Province of China （2016J01280）.
文摘In this paper, we propose a new hybrid method called SQPBSA which combines backtracking search optimization algorithm （BSA） and sequential quadratic programming （SQP）. BSA, as an exploration search engine, gives a good direction to the global optimal region, while SQP is used as a local search technique to exploit the optimal solution. The experiments are carried on two suits of 28 functions proposed in the CEC-2013 competitions to verify the performance of SQPBSA. The results indicate the proposed method is effective and competitive.
