基于高斯伪谱法的月球软着陆轨道的数值分析

月球软着陆是月球探测技术发展的关键所在,针对月球软着陆过程中的着陆轨道设计、着陆过程最优控制的实际问题进行定量分析,运用高斯伪谱法将问题转化为求解满足一定性能指标和约束条件的最优控制问题以确定月球软着陆的最优控制策略;分析着陆区域数字高程图,得到月球最优着陆区域.     

一类最优控制问题的最大值原理

本文研究具有终端约束的最优控制问题.利用构造罚函数的方法将其转化为无约束的近似问题,从而证明最优控制的存在性及其所满足的最大值原理.     

求解线性规划的极大熵方法

极大熵方法是求解多约束非线性规划和极大极小问题的一种有效的方法.用它来求解多约束优化问题,一种途径是将多约束用单约束近似,再用增广Lagrange乘子法求解近似问题;另一种途径是用极大熵方法构造精确罚函数的近似.无论是哪一种途径都需要估计乘子的上界.能否构造不引入乘子估计的算法是很有意义的.Karmarkar算法是求解线性规划的一种有效的多项式内点方法.这种方法在每一次迭代时都要作变换,在像空间用内切球近似单纯形的近似问题得到像空间的新的近似解,再作逆变换求得原空间的新的近似解.可见一次性地构造近似问题并求解之而得     

具有潜伏期时滞的时变SEIR模型的最优疫苗接种策略

该文在经典SEIR仓室模型的基础上，在由潜伏个体转化为感染个体的过程中，引入了时滞参数以刻画潜伏期的特性.同时，将传染系数改写为季节性变化参数，并通过引入疫苗接种和时变的成功免疫率，形成了含有时滞受控的时变SEIR模型.进一步地，在状态时滞最优控制问题的框架下，以疫苗接种率为控制变量，求解了基于该模型的传染病最优疫苗接种策略.在最优控制问题中，同时考虑了控制约束、易感染人口数上限、时变的疫苗产量上限三类约束.使用多区段的保辛伪谱方法对该问题进行求解.数值结果表明，计算得到的控制策略可以有效抑制传染病的传播.不同算例之间的对比说明忽略时变因素可能导致不合理的接种策略.     

最优控制问题参数化研究—基于勒让德正交多项式逼近

使用勒让德正交多项式逼近方法,将Lagrange型最优控制问题转化为非线性规划问题.采用序列二次规划方法对此非线性规划问题的求解,并对多项式逼近和非线性规划求解后得到的解是否收敛给出了证明和实例分析.     

基于符号数值混合计算的混成系统Lyapunov函数构造

基于平方和松弛和有理向量恢复,提出了一种符号数值混合计算方法来构造多项式Lyapunov函数以判定非线性混成系统的稳定性,首先,为Lyapunov函数预定一个给定次数的多项式模板,则Lyapunov函数构造问题可转化为相应的带参数的多项式优化问题,然后运用平方和松弛方法求得一个近似的数值多项式Lyapunov函数,再应用高斯-牛顿精化和有理向量恢复将数值多项式转化为验证的有理多项式Lyapunov函数.     

ATCase:一个基于多项式约束求解的数值程序测试用例自动生成工具北大核心CSCD

现有的基于符号执行的测试用例自动生成技术存在不足之处:由于精度限制和非线性约束求解的复杂性,符号执行在遇到复杂的非线性浮点约束时效果并不理想.针对这一现状,给出了一个基于多项式约束求解和区间验证的测试用例生成算法.对于复杂非线性约束难以求解的问题,采用基于低秩矩量矩阵恢复的多项式系统求解方法,该方法对于含有等式和不等式的多项式系统,相较于其他方法求解速度更快,更适合大规模问题的求解;对于浮点约束求解不准确的问题,采用基于区间分析的验证算法来计算包含精确实解的区间,基于该区间给出测试用例,可以避免浮点计算的不准确和异常.结合该算法和符号执行工具KLEE-FP实现了一个测试用例自动生成工具ATCase(automatically generate test case),它能够分析数值程序中的路径并自动生成满足路径约束的测试用例.在两个开源软件库中的2两个复杂的真实程序上运行的实验结果表明ATCase相比KLEE-FP所使用的STP求解器,能快速生成具有更高覆盖率的测试用例,特别是在处理相对复杂的非线性约束时,优势更加明显.     

ATCase:一个基于多项式约束求解的数值程序测试用例自动生成工具北大核心CSCD

现有的基于符号执行的测试用例自动生成技术存在不足之处:由于精度限制和非线性约束求解的复杂性,符号执行在遇到复杂的非线性浮点约束时效果并不理想.针对这一现状,给出了一个基于多项式约束求解和区间验证的测试用例生成算法.对于复杂非线性约束难以求解的问题,采用基于低秩矩量矩阵恢复的多项式系统求解方法,该方法对于含有等式和不等式的多项式系统,相较于其他方法求解速度更快,更适合大规模问题的求解;对于浮点约束求解不准确的问题,采用基于区间分析的验证算法来计算包含精确实解的区间,基于该区间给出测试用例,可以避免浮点计算的不准确和异常.结合该算法和符号执行工具KLEE-FP实现了一个测试用例自动生成工具ATCase(automatically generate test case),它能够分析数值程序中的路径并自动生成满足路径约束的测试用例.在两个开源软件库中的2两个复杂的真实程序上运行的实验结果表明ATCase相比KLEE-FP所使用的STP求解器,能快速生成具有更高覆盖率的测试用例,特别是在处理相对复杂的非线性约束时,优势更加明显.     

ATCase:一个基于多项式约束求解的数值程序测试用例自动生成工具北大核心CSCD

现有的基于符号执行的测试用例自动生成技术存在不足之处:由于精度限制和非线性约束求解的复杂性,符号执行在遇到复杂的非线性浮点约束时效果并不理想.针对这一现状,给出了一个基于多项式约束求解和区间验证的测试用例生成算法.对于复杂非线性约束难以求解的问题,采用基于低秩矩量矩阵恢复的多项式系统求解方法,该方法对于含有等式和不等式的多项式系统,相较于其他方法求解速度更快,更适合大规模问题的求解;对于浮点约束求解不准确的问题,采用基于区间分析的验证算法来计算包含精确实解的区间,基于该区间给出测试用例,可以避免浮点计算的不准确和异常.结合该算法和符号执行工具KLEE-FP实现了一个测试用例自动生成工具ATCase(automatically generate test case),它能够分析数值程序中的路径并自动生成满足路径约束的测试用例.在两个开源软件库中的2两个复杂的真实程序上运行的实验结果表明ATCase相比KLEE-FP所使用的STP求解器,能快速生成具有更高覆盖率的测试用例,特别是在处理相对复杂的非线性约束时,优势更加明显.     

ATCase:一个基于多项式约束求解的数值程序测试用例自动生成工具

现有的基于符号执行的测试用例自动生成技术存在不足之处:由于精度限制和非线性约束求解的复杂性,符号执行在遇到复杂的非线性浮点约束时效果并不理想.针对这一现状,给出了一个基于多项式约束求解和区间验证的测试用例生成算法.对于复杂非线性约束难以求解的问题,采用基于低秩矩量矩阵恢复的多项式系统求解方法,该方法对于含有等式和不等式的多项式系统,相较于其他方法求解速度更快,更适合大规模问题的求解;对于浮点约束求解不准确的问题,采用基于区间分析的验证算法来计算包含精确实解的区间,基于该区间给出测试用例,可以避免浮点计算的不准确和异常.结合该算法和符号执行工具KLEE-FP实现了一个测试用例自动生成工具ATCase(automatically generate test case),它能够分析数值程序中的路径并自动生成满足路径约束的测试用例.在两个开源软件库中的2两个复杂的真实程序上运行的实验结果表明ATCase相比KLEE-FP所使用的STP求解器,能快速生成具有更高覆盖率的测试用例,特别是在处理相对复杂的非线性约束时,优势更加明显.     

Collocation points distributions for optimal spacecraft trajectories

The method of direct collocation with nonlinear programming (DCNLP) is a powerful tool to solve optimal control problems (OCP). In this method the solution time history is approximated with piecewise polynomials, which are constructed using interpolation points deriving from the Jacobi polynomials. Among the Jacobi polynomials family, Legendre and Chebyshev polynomials are the most used, but there is no evidence that they offer the best performance with respect to other family members. By solving different OCPs with interpolation points not only taken within the Jacoby family, the behavior of the Jacobi polynomials in the optimization problems is discussed. This paper focuses on spacecraft trajectories optimization problems. In particular orbit transfers, interplanetary transfers and station keepings are considered.     

Filtration problem in inhomogeneous dam by using embedding method

This paper is devoted to a new numerical technique for the approximation of the flow problem of incompressible liquid through an inhomogeneous porous medium (say dam). First the problem is expressed as an optimal control problem governed by variational forms on a fixed domain. Then by using an embedding method, the class of admissible shapes is replaced by a class of positive Radon measures. The optimization problem in measure space is then approximated by a linear programming problem. The optimal measure representing optimal shape is approximated by the solution of this linear programming problem. Numerical example is also given.     

Asymptotic Analysis of Sample Average Approximation for Stochastic Optimization Problems with Joint Chance Constraints via Conditional Value at Risk and Difference of Convex Functions

Conditional Value at Risk (CVaR) has been recently used to approximate a chance constraint. In this paper, we study the convergence of stationary points, when sample average approximation (SAA) method is applied to a CVaR approximated joint chance constrained stochastic minimization problem. Specifically, we prove under some moderate conditions that optimal solutions and stationary points, obtained from solving sample average approximated problems, converge with probability one to their true counterparts. Moreover, by exploiting the recent results on large deviation of random functions and sensitivity results for generalized equations, we derive exponential rate of convergence of stationary points. The discussion is also extended to the case, when CVaR approximation is replaced by a difference of two convex functions (DC-approximation). Some preliminary numerical test results are reported.     

Symbolic-numeric efficient solution of optimal control problems for multibody systems

This paper presents an efficient symbolic-numerical approach for generating and solving the boundary value problem-differential algebraic equation (BVP-DAE) originating from the variational form of the optimal control problem (OCP). This paper presents the method for the symbolic derivation, by means of symbolic manipulation software (Maple), of the equations of the OCP applied to a generic multibody system. The constrained problem is transformed into a nonconstrained problem, by means of the Lagrange multipliers and penalty functions. From the first variation of the nonconstrained problem a BVP-DAE is obtained, and the finite difference discretization yields a nonlinear systems. For the numerical solution of the nonlinear system a damped Newton scheme is used. The sparse and structured Jacobians is quickly inverted by exploiting the sparsity pattern in the solution strategy. The proposed method is implemented in an object oriented fashion, and coded in C++ language. Efficiency is ensured in core routines by using Lapack and Blas for linear algebra.     

Hybrid functions approach for nonlinear constrained optimal control problems

In this paper, a new numerical method for solving the nonlinear constrained optimal control with quadratic performance index is presented. The method is based upon hybrid functions approximation. The properties of hybrid functions consisting of block-pulse functions and Bernoulli polynomials are presented. The operational matrix of integration is introduced. This matrix is then utilized to reduce the solution of the nonlinear constrained optimal control to a nonlinear programming one to which existing well-developed algorithms may be applied. Illustrative examples are included to demonstrate the validity and applicability of the technique.     

Pseudospectral Legendre-based optimal computation of nonlinear constrained variational problems

It is well known that, spectrally accurate solution can be maintained if the grids on which a nonlinear physical problem is to be solved must be obtained by spectrally accurate techniques. In this paper, the pseudospectral Legendre method for general nonlinear smooth and nonsmooth constrained problems of the calculus of variations is studied. The technique is based on spectral collocation methods in which the trajectory, x(t), is approximated by the Nth degree interpolating polynomial, using Legendre-Gauss-Lobatto points as the collocation points, and Lagrange polynomials as trial functions. The integral involved in the formulation of the problem is approximated based on Legendre-Gauss-Lobatto integration rule, thereby reducing the problem to a nonlinear programming one to which existing well-developed algorithms may be applied. The method is easy to implement and yields very accurate results. Illustrative examples are included to confirm the convergence of the pseudospectral Legendre method. Moreover, a numerical experiment (on a nonsmooth problem) indicates that by applying a smoothing filter procedure to the pseudospectral Legendre approximation, one can recover the nonsmooth solution within spectral accuracy.     

Superconvergence analysis of fully discrete finite element methods for semilinear parabolic optimal control problems

We study the superconvergence property of fully discrete finite element approximation for quadratic optimal control problems governed by semilinear parabolic equations with control constraints. The time discretization is based on difference methods, whereas the space discretization is done using finite element methods. The state and the adjoint state are approximated by piecewise linear functions and the control is approximated by piecewise constant functions. First, we define a fully discrete finite element approximation scheme for the semilinear parabolic control problem. Second, we derive the superconvergence properties for the control, the state and the adjoint state. Finally, we do some numerical experiments for illustrating our theoretical results.     

An adaptive parameter approach for an approximate solution of optimal control problems

In this paper, we consider a class of nonlinear optimal control problems (Bolza-problems) with constraints of the control vector, initial and boundary conditions of the state vectors. The time interval is fixed. Our approach to parametrize both the state functions and the control functions is described by general piecewise polynomials with unknown coefficients (parameters), where a fixed partition of the time interval is used. Here each of these functions in a suitable way individually will be approximated by such polynomials. The optimal control problem thus is reduced to a mathematical programming problem for these parameters. The existence of an optimal solution is assumed. Convergence properties of this method are not considered in this paper.     

半线性抛物最优控制问题全离散插值系数有限元方法的收敛性分析

本文考虑全离散插值系数有限元方法求解半线性抛物最优控制问题,其中控制变量用分片常数函数逼近,状态变量和对偶状态变量用分片线性函数逼近.对于方程中的半线性项,先用插值系数技巧处理,再用牛顿迭代法求解.通过引入一些辅助变量和投影算子,并利用有限元空间的逼近性质,得到半线性抛物最优控制问题插值系数有限元方法的收敛性结果;数值算例结果验证了理论结果的正确性.     

A penalty function-based random search algorithm for optimal control of switched systems with stochastic constraints and its application in automobile test-driving with gear shifts

Practical industrial process is usually a dynamic process including uncertainty. Stochastic constraints can be used for industrial process modeling, when system sate and/or control input constraints cannot be strictly satisfied. Thus, optimal control of switched systems with stochastic constraints can be available to address practical industrial process problems with different modes. In general, obtaining an analytical solution of the optimal control problem is usually very difficult due to the discrete nature of the switching law and the complexity of stochastic constraints. To obtain a numerical solution, this problem is formulated as a constrained nonlinear parameter selection problem (CNPSP) based on a relaxation transformation (RT) technique, an adaptive sample approximation (ASA) method, a smooth approximation (SA) technique, and a control parameterization (CP) method. Following that, a penalty function-based random search (PFRS) algorithm is designed for solving the CNPSP based on a novel search rule-based penalty function (NSRPF) method and a novel random search (NRS) algorithm. The convergence results show that the proposed method is globally convergent. Finally, an optimal control problem in automobile test-driving with gear shifts (ATGS) is further extended to illustrate the effectiveness of the proposed method by taking into account some stochastic constraints. Numerical results show that compared with other typical methods, the proposed method is less conservative and can obtain a stable and robust performance when considering the small perturbations in initial system state. In addition, to balance the computation amount and the numerical solution accuracy, a tolerance setting method is also provided by the numerical analysis technique.