基于并行处理的线性二次型最优控制器

本文研究基于并行处理的线性二次型最优控制器问题。文中提出了一种平方根线性二次型最优控制算法，并将其映射到并行专用结构──脉动阵列（ｓｙｓｔｏｌｉｃａｒｒａｙ）上。加快了控制器参数综合，为快速动态环境下最优控制的实时应用开辟了广阔的前景。     

离散Markov跳变线性系统最优控制

针对离散Markov跳变系统,研究其最优控制问题。首先确立一个二次型代价函数,然后运用随机贝尔曼动态规划法,结合Markov跳变系统特性求解贝尔曼方程,获得了完全状态信息情形下Markov跳变系统的最优控制器和黎卡提差分方程;进而将其推广到不完全状态信息情形,利用观测向量获得状态的后验概率密度函数,推导了最优控制器的解析结构和相应的求解算法;最后通过数值仿真验证了所得控制器的有效性。     

方块脉冲函数用于PWM系统最优控制分析与综合

本文基于方块脉冲函数理论，分析了脉宽调制系统的精确方法，给出了便于计算机求解的递推算式，将ＬＱＰ（线性二次型问题）最优控制问题转化为求解多元函数最优化问题，避开了Ｒｉｃｃａｔｉ方程的求解，得到了状态反馈形式的分段综合控制表示式，以及最优控制的分段恒定解和最优状态轨线。     

考虑人寿保险的最优金融决策

不同于以前的最优消费、投资问题研究，本文研究个人投资者的最优金融决策问题——如何决定最优的证券组合、消费和购买人寿保险，使其期望效用最大化。假设投资者死亡事件是一个独立的泊松过程，对投资者生存期间的最优金融决策问题构造了一数学随机模型，运用动态规划原理和随机分析方法，解决对应的最优控制问题，最优策略可通过对应的HJB方程得到。对于效用函数为CRRA(常数相对风险厌恶)类型，显式地得到具有反馈形式的最优投资过程、消费过程及人寿保险购买过程。     

非完全市场最优消费和投资策略研究

在连续时间模型假设下，研究风险资产价格服从一个带有随机方差几何布朗运动的最优消费和投资问题。首先建立了最优消费和投资问题随机最优控制数学模型；然后运用随机最优控制理论，得到了最优消费和投资随机最优控制问题的值函数所满足的偏微分方程，最后与经典Merton问题进行了比较。     

违约相关性下包含信用债券的最优投资组合

研究了当信用债券之间的违约存在相关性时, 投资者配置于信用债券组合、股票(股指)和银行存款的最优投资组合问题. 利用简约化模型来刻画信用债券之间的违约相关性, 并推导出各资产价格的动态方程, 通过随机控制方法给出了此优化问题的解析解. 将违约相关性引入至投资组合并揭示了其对最优投资组合的影响是本文与以往文献的显著不同和主要创新.     

常弹性方差模型下保险人的最优投资策略

假设风险资产价格服从常弹性方差(CEV)模型, 保险人面临的风险过程是带漂移的布朗运动. 投资过程与承保风险过程完全相关. 根据随机最优控制理论, 建立保险基金投资问题的HJB方程. 由于该方程是非线性偏微分方程, 不易求解, 因此采用Legendre变换将其转换成对偶问题进行研究. 最后针对特定参数值分别得到以CARA和CRRA效用函数为目标的保险人的最优投资策略, 这样的投资策略更符合金融市场的实际要求.     

具有模型和实际差异的非线性离散时滞系统最优控制

研究了非线性分布时滞系统的最优控制,提出了一种基于线性分布时滞模型和二次型性能指标问题的迭代算法。在模型和实际存在差异的情况下,该算法通过迭代求解分布时滞线性最优控制问题和参数估计问题,获得原问题的最优解。仿真实例表明该算法的有效性和实用性。     

固定消费模式下的最优投资组合

研究了当投资者的消费为固定模式时的最优投资组合问题．投资者的目的是：在保证固定消费正常进行的条件下，使最终财富的期望效用最大．把现金流分成两部分来考虑：一部分保证消费的正常进行，一部分用于投资．假设投资者的消费是时间的连续函数或者分段连续函数，应用随机最优控制的方法得到了这两种情形下一般效用函数的最优投资策略并导出了值函数满足的HJB方程,最后，分析了消费对投资决策的影响.     

基于粒子群算法的开关系统最优控制问题的数值算法

针对开关系统,给出了数学模型并引出了其最优控制问题,提出开关系统最优控制问题的加权粒子群算法,给出了相关的推理过程及算法步骤。加权粒子群算法不必找出支付泛函关于时间的显式表达,就可以找到其最优解,同样适用于其子系统为非线性的情形。分析了粒子群算法快速全局优化的特点,说明该算法能找到优化问题的全局最优解。以开关动态系统和一般开关线性二次问题的数值算例验证了该方法的有效性。     

FORWARD-BACKWARD STOCHASTIC DIFFERENTIAL EQUATIONS, LINEAR QUADRATIC STOCHASTIC OPTIMAL CONTROL AND NONZERO SUM DIFFERENTIAL GAMES

In this paper, we use the solutions of forward-backward stochastic differential equations to get the explicit form of the optimal control for linear quadratic stochastic optimal control problem and the open-loop Nash equilibrium point for nonzero sum differential games problem. We also discuss the solvability of the generalized Riccati equation system and give the linear feedback regulator for the optimal control problem using the solution of this kind of Riccati equation system.     

CEV模型下基于双曲绝对风险厌恶效用的最优投资策略

研究双曲绝对风险厌恶(HARA)型投资者在常弹性方差(CEV)模型下面临完全可对冲随机资金流时的最优动态资产配置问题.随机资金流可以视作一个外生负债,假定其服从带漂移的布朗运动.根据随机控制理论建立该问题的哈密顿-雅克比-贝尔曼(HJB)方程,通过猜测值函数的代数形式,将其化简为两个抛物型偏微分方程并分别求得显式解,从而得到最优投资策略.结果表明该非自融资组合的最优动态配置问题等价于初始财富为所有未来随机净资金流在风险中性测度下累积期望现值与初始稟赋之和的自融资组合的最优动态配置问题.投资策略由短视投资策略,动态对冲策略,静态对冲策略三部分组成.当对模型中参数取特殊值时,策略简化为已有文献的相应结果.最后分析了参数变化对于由随机资金流引起的额外投资需求的影响.     

A Mean-Field Optimal Control for Fully Coupled Forward-Backward Stochastic Control Systems with Lévy Processes

This paper is concerned with a class of mean-field type stochastic optimal control systems, which are governed by fully coupled mean-field forward-backward stochastic differential equations with Teugels martingales associated to Lévy processes. In these systems, the coefficients contain not only the state processes but also their marginal distribution, and the cost function is of mean-field type as well. The necessary and sufficient conditions for such optimal problems are obtained. Furthermore, the applications to the linear quadratic stochastic optimization control problem are investigated.

     

带有通胀风险的退休后期最优投资管理

考虑通货膨胀的影响,研究了一个确定缴费养老计划退休后期最优投资决策问题.自退休时刻开始,退休者定期从账户里抽取一定的金额维持日常支出,然后将剩余的财富投资于一个无风险资产、一个股票指数和一个通胀指数债券,直到强制购买年金的时刻.为保障退休后的正常生活,退休者在每个时刻设定投资的目标值,采取二次效用函数衡量投资财富水平和目标值的差距,并选择最优的投资策略以最小化平均累计差距.运用动态规划和随机控制方法,得到了没有上方惩罚的目标值、最优投资策略、最优值函数、破产概率以及终端财富与目标值差距的分布函数等指标的显式表达式.运用数学分析和数值分析手段,得到了每个时刻目标值的性质,分析了终端目标值和消费金额对破产概率的影响,研究了物价指数的瞬间变化率和波动率对财富值与目标值的差距、各时刻财富均值以及破产概率的影响.     

The Robust H∞ Control of a Stochastic Inventory—Production System

1　 Introduction DealsMost inventory- production models assume that the demands are influenced by randomlyfluctuating environment.Under the assumption of probability distribution being known,many researches on inventory optimization and control problem ha…     

Stochastic differential equations and stochastic linear quadratic optimal control problem with Lévy processes

In this paper, the authors first study two kinds of stochastic differential equations (SDEs) with Lévy processes as noise source. Based on the existence and uniqueness of the solutions of these SDEs and multi-dimensional backward stochastic differential equations (BSDEs) driven by Lévy processes, the authors proceed to study a stochastic linear quadratic (LQ) optimal control problem with a Lévy process, where the cost weighting matrices of the state and control are allowed to be indefinite. One kind of new stochastic Riccati equation that involves equality and inequality constraints is derived from the idea of square completion and its solvability is proved to be sufficient for the well-posedness and the existence of optimal control which can be of either state feedback or open-loop form of the LQ problems. Moreover, the authors obtain the existence and uniqueness of the solution to the Riccati equation for some special cases. Finally, two examples are presented to illustrate these theoretical results. This work was supported by the National Basic Research Program of China (973 Program) under Grant No. 2007CB814904, the Natural Science Foundation of China under Grant No. 10671112 and Shandong Province under Grant No. Z2006A01, and Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20060422018.     

A branch-and-cut approach to portfolio selection with marginal risk control in a linear conic programming framework

Marginal risk represents the risk contribution of an individual asset to the risk of the entire portfolio In this paper, we investigate the portfolio selection problem with direct marginal risk control in a linear conic programming framework. ＇The optimization model involved is a nonconvex quadratically constrained quadratic programming （QCQP） problem. We first transform the QCQP problem into a linear conic programming problem, and then approximate the problem by semidefinite programming （SDP） relaxation problems over some subrectangles. In order to improve the lower bounds obtained from the SDP relaxation problems, linear and quadratic polar cuts are introduced for designing a branch-and-cut algorithm, that may yield an e -optimal global solution （with respect to feasibility and optimality） in a finite number of iterations. By exploring the special structure of the SDP relaxation problems, an adaptive branch-and-cut rule is employed to speed up the computation. The proposed algorithm is tested and compared with a known method in the literature for portfolio selection problems with hundreds of assets and tens of marginal risk control constraints.     

Receding horizon H∞ control for discrete-time Markovian jump linear systems

Receding horizon H∞ control scheme which can deal with both the H∞ disturbance attenuation and mean square stability is proposed for a class of discrete-time Markovian jump linear systems when minimizing a given quadratic performance criteria. First, a control law is established for jump systems based on pontryagin’s minimum principle and it can be constructed through numerical solution of iterative equations. The aim of this control strategy is to obtain an optimal control which can minimize the cost function under the worst disturbance at every sampling time. Due to the difficulty of the assurance of stability, then the above mentioned approach is improved by determining terminal weighting matrix which satisfies cost monotonicity condition. The control move which is calculated by using this type of terminal weighting matrix as boundary condition naturally guarantees the mean square stability of the closed-loop system. A sufficient condition for the existence of the terminal weighting matrix is presented in linear matrix inequality (LMI) form which can be solved efficiently by available software toolbox. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method.