基于SDRE具有终端碰撞角约束的非线性微分对策制导律

通过非线性微分对策理论讨论了具有终端碰撞角约束的弹目追逃问题,提出了基于求解状态相关黎卡提方程(state dependent Riccati equation,SDRE)的方法解决该微分对策问题,得到了具有终端碰撞角约束的SDRE解析解。提出的终端碰撞角约束制导律是以弹目视线角速率及碰撞角误差为状态向量进行推导,并研究了闭环系统局部渐进稳定的条件。该制导律不需要进行剩余时间的预测。最后针对目标不进行机动、进行阶跃机动、正弦机动及目标最优机动形式4种情况,进行了制导律的仿真验证,仿真结果表明该制导律对于不同机动目标均具有良好的制导效果且能很好地满足末端碰撞角约束要求。     

用混沌搜索求解非线性约束优化问题

提出了一种用混沌搜索求解非线性约束优化的新方法 .利用罚函数思想将约束问题无约束化 ,再利用混沌的内在随机性与遍历性进行求解 .算例仿真结果表明 ,算法简单实用 ,性能良好 ,是解决非线性约束优化问题的有效途径 .     

Multiobjective Imperialist Competitive Algorithm for Solving Nonlinear Constrained Optimization Problems

Nonlinear constrained optimization problem(NCOP) has been arisen in a diverse range of sciences such as portfolio, economic management, airspace engineering and intelligence system etc.In this paper, a new multiobjective imperialist competitive algorithm for solving NCOP is proposed.First, we review some existing excellent algorithms for solving NOCP; then, the nonlinear constrained optimization problem is transformed into a biobjective optimization problem. Second, in order to improve the diversity of evolution country swarm, and help the evolution country swarm to approach or land into the feasible region of the search space, three kinds of different methods of colony moving toward their relevant imperialist are given. Thirdly, the new operator for exchanging position of the imperialist and colony is given similar as a recombination operator in genetic algorithm to enrich the exploration and exploitation abilities of the proposed algorithm. Fourth, a local search method is also presented in order to accelerate the convergence speed. At last, the new approach is tested on thirteen well-known NP-hard nonlinear constrained optimization functions, and the experiment evidences suggest that the proposed method is robust, efficient, and generic when solving nonlinear constrained optimization problem. Compared with some other state-of-the-art algorithms, the proposed algorithm has remarkable advantages in terms of the best, mean, and worst objective function value and the standard deviations.     

Multiobjective evolutionary algorithm for dynamic nonlinear constrained optimization problems

A new method to solve dynamic nonlinear constrained optimization problems (DNCOP) is proposed. First, the time (environment) variable period of DNCOP is divided into several equal subperiods. In each subperiod, the DNCOP is approximated by a static nonlinear constrained optimization problem (SNCOP). Second, for each SNCOP, inspired by the idea of multiobjective optimization, it is transformed into a static bi-objective optimization problem. As a result, the original DNCOP is approximately transformed into several static bi-objective optimization problems. Third, a new multiobjective evolutionary algorithm is proposed based on a new selection operator and an improved nonuniformity mutation operator. The simulation results indicate that the proposed algorithm is effective for DNCOP.     

求解非线性双层规划问题的混合变邻域粒子群算法

针对非线性双层规划难以获得全局最优的问题,汲取粒子群算法的快速搜索能力及变邻域搜索算法的全局搜索优势,提出了求解非线性双层规划问题的混合变邻域粒子群算法.首先利用Kuhn-Tucker条件,将非线性双层规划转化为一个单层规划问题,然后由粒子群算法得到一个较优的群体,通过审敛因子判断陷入局部最优的粒子,并进一步利用变邻域搜索算法的全局搜索能力对陷入局部最优的粒子进行优化,从而得到全局最优.测试函数的仿真实验对比分析证明了该算法的有效性.     

Bilevel programming model and solution method for mixed transportation network design problem

By handling the travel cost function artfully, the authors formulate the transportation mixed network design problem (MNDP) as a mixed-integer, nonlinear bilevel programming problem, in which the lower-level problem, comparing with that of conventional bilevel DNDP models, is not a side constrained user equilibrium assignment problem, but a standard user equilibrium assignment problem. Then, the bilevel programming model for MNDP is reformulated as a continuous version of bilevel programming problem by the continuation method. By virtue of the optimal-value function, the lower-level assignment problem can be expressed as a nonlinear equality constraint. Therefore, the bilevel programming model for MNDP can be transformed into an equivalent single-level optimization problem. By exploring the inherent nature of the MNDP, the optimal-value function for the lower-level equilibrium assignment problem is proved to be continuously differentiable and its functional value and gradient can be obtained efficiently. Thus, a continuously differentiable but still nonconvex optimization formulation of the MNDP is created, and then a locally convergent algorithm is proposed by applying penalty function method. The inner loop of solving the subproblem is mainly to implement an all-or-nothing assignment. Finally, a small-scale transportation network and a large-scale network are presented to verify the proposed model and algorithm. This research is supported by the National Basic Research Program of China under Grant No. 2006CB705500, the National Natural Science Foundation of China under Grant No. 0631001, the Program for Changjiang Scholars and Innovative Research Team in University, and Volvo Research and Educational Foundations.     

SET COVERING-BASED TOPSIS METHOD FOR SLOVING SUP-T EQUATION CONSTRAINED MULTI-OBJECTIVE OPTIMIZATION PROBLEMS

This paper considers solving a multi-objective optimization problem with sup-r equation constraints.A set covering-based technique for order of preference by similarity to the ideal solution is proposed for solving such a problem.It is shown that a compromise solution of the sup-r equation constrained multi-objective optimization problem can be obtained by solving an associated set covering problem.A surrogate heuristic is then applied to solve the resulting optimization problem.Numerical experiments on solving randomly generated multi-objective optimization problems with sup-T equation constraints are included.Our computational results confirm the efficiency of the proposed method and show its potential for solving large scale sup-T equation constrained multi-objective optimization problems.     

时间序列模型辨识的非线性规划方法

本文利用有关优化理论,把ARMA模型辩识问题转化为带约束条件的非线性规划问题,并通过降维处理把n阶的ARMA模型辨识问题简化为3n维的无约束非线性规划问题,从而为一般的ARMA模型辨识提供了一条新的途径。文中还给出了仿真例子,以证明该方法的有效性。     

基于自适应未知输入观测器的多故障快速重构

针对一类非线性函数中耦合执行器故障的非线性动态系统, 提出一种基于自适应未知输入观测器的多故障快速重构方法, 通过引入比例项提高故障重构的快速性。首先, 将执行器故障进行解耦处理并构建包含传感器故障的增广系统。然后, 综合H_∞性能指标给出状态估计误差的稳定性证明。接着, 将观测器增益矩阵的求解转化为受线线矩阵不等式约束的非线性优化问题, 并实现执行器故障和传感器故障的多故障重构。最后, 结合单关节柔性机器人算例仿真验证了所提方法的有效性。     

An Application of the Stability Theory in Constrained Nonlinear Optimization

1　 IntroductionIt is known that a given constrained nonlinear optimization problem can be transformedand solved by ordinary differential equations(ODE) [4,5] .Based on the preliminary resultsof [8～ 1 2 ] ,this paper uses a stability theory of ODE systems to analyze the asymptoticalproperties of differential equations which are derived from a given constrained nonlinearoptimization problem.We also state conditions of the overall uniform asymptoticalstability of the singular points aboutpart…     

舰载机着舰非线性反演控制方法研究进展

现在舰载机着舰控制以线性方法为主,而着舰的非线性特征和舰尾流等外部扰动可能导致线性方法失效,因此有待进一步研究非线性方法在着舰控制领域的应用。首先对非线性控制方法在舰载机着舰中的应用进行了综述,总结了各类非线性方法在着舰应用的优势及不足;然后针对反演这一非线性方法进行了重点概述:在分析舰载机传统反演方法不足的基础上,结合舰载机着舰过程中因为外界环境扰动和雷达噪声等因素可能引发的舵面受限,重点总结了受限指令反演方法在一般飞控的研究进展,研究了受限指令反演方法的优势和不足,探讨了将受限指令反演方法引入着舰控制的可能性;最后对未来发展方向进行了展望。     

非线性约束最短路问题的启发式算法

多约束QoS路由优化是当前网络研究中的一个重要课题,而受限最短路问题(RSP)是QoS路由的一个基本问题。它是NP-完全的,并有许多具有多项式时间和伪多项式时间的启发式求解算法。然而这些方法只能求解一些带有线性约束的RSP。对一些非线性的约束(比如丢失率约束)大都用数学方法转化成线性约束来求解,这增加了问题的复杂性。本文提出了一种新的具有伪多项式时间的启发式算法来求解这类带非线性约束的RSP。主要思想是将非线性约束作为检验条件来使用。当每得到一个解时,检查解是否满足非线性约束。如满足,则得到最终解;否则在原问题中添加一个线性约束。该新约束将去除已经找到的解,从而使原问题的解空间进一步缩小,直到得到最终解。仿真算例说明了算法的有效性。     

A simple smooth exact penalty function for smooth optimization problem

For smooth optimization problem with equality constraints,new continuously differentiate penalty function is derived.It is proved exact in the sense that local optimizers of a nonlinear program are precisely the optimizers of the associated penalty function under some nondegeneracy assumption. It is simple in the sense that the penalty function only includes the objective function and constrained functions,and it doesn’t include their gradients.This is achieved by augmenting the dimension of the program by a variable that controls the weight of the penalty terms.     

弹性高超声速飞行器智能控制系统设计

针对气动舵受限下的弹性高超声速飞行器控制问题, 提出一种基于神经自适应的智能控制方案。在速度子系统的设计过程中, 为了降低对模型参数的依赖程度, 应用强化学习算法在线调整比例积分微分(proportional integral derivative, PID)控制参数, 给出智能PID控制策略。对于高度子系统, 考虑气动舵的动态特性, 利用神经自适应方法对模型未知函数及不确定项进行逼近。为了处理气动舵的约束问题, 以非线性模型预测控制为优化分配模板生成大量样本数据集, 经离线训练得到深度神经网络代替求解复杂优化问题和控制分配的过程。此外, 通过引入自适应超螺旋微分器处理外部扰动, 增强了系统的鲁棒性。利用Lyapunov方法证明了所设计控制器的稳定性, 并通过仿真验证了所设计控制方案能够快速计算控制指令, 实现高精度跟踪控制。     

模糊多目标有约束投资项目选择建模

基于模糊多目标评价 ,对有约束条件的多个投资项目的选择问题 ,建立了一个模糊规划模型 ,利用一种对模糊数排序的解模糊函数 ,对集结成的各项目的非线性模糊综合评价值解模糊 ,将模糊规划模型转化为线性规划模型求解 ,最后给出了一个例子.     

具有时间窗口约束的并行机床调度问题研究

具有时间窗口约束的并行机床调度问题可以被描述为在M台互不相同机器上安排加工N个工件。每个工件只能在一个给定机器子集中的一台机器上加工，且加工过程只能在给定的离散时间窗口内进行，并且工件的加工时间长度和时间窗口约束与加工机器有关。为了解决该问题，本文给出一种集成约束规划与禁忌搜索的搜索算法，其中约束规划系统被作为验证解的可行性和确定变量的值，而禁忌搜索过程被用来对解空间进行搜索。讨论禁忌搜索过程中的初始解产生、邻域结构、禁忌列表、解空间搜索等算法设计问题。最后给出算法在不同问题实例上的算法效率比较及分析。     

基于可靠性的软件构件费用分配最优模型

针对软件系统可靠性和费用分配问题,给出了一种基于可靠性的软件构件费用分配最优模型。将软件系统可靠性定义为软件构件失效率、操作剖面、构件使用矩阵以及软件无失效运行的规定任务时间的函数,详细描述了费用最优模型的建立和利用非线性规划理论求解模型的步骤。在求解过程中,将费用最优模型转化为Kuhn-Tucker方程,有效地处理了带有复杂计算的目标函数和约束条件的可靠性和费用最优分配问题。计算实例表明利用该模型进行可靠性和费用分配是可行的。     

双参数精确罚函数求解约束优化问题的拟牛顿算法

对于含约束不等式的最优化问题,给出了一种双参数罚函数形式和这种罚函数的精确罚定理,提出了一个求解这种罚函数无约束优化问题的拟牛顿算法,研究了它的收敛性,数值实验表明了该算法是可行的.     

多目标主从向量集值优化Benson真有效解的最优性条件

借助 Aubin的 Contingent切导数建立了可微多目标主从向量集值优化弱 Benson真有效元的Fritz-John型最优性条件.     

基于Volterra级数的全解耦RLS自适应辨识算法

针对非线性系统辨识问题,提出了一种基于Volterra级数模型的非线性系统的全解耦RLS自适应辨识算法。按照Volterra伪线性组合结构,采用RLS自适应辨识和约束优化理论,导出了具有分块对角形输入相关矩阵的全解耦Volterra标准方程,据此设计了一种基于Volterra级数模型的全解耦的RLS自适应辨识算法。该算法与部分解耦的RLS自适应算法相比,显著提高了辨识过程的收敛速度和精度。仿真结果验证了该方法的有效性。