Minimax-statistical approach to increasing reliability of measurement information processing

Consideration was given to the methods of guaranteeing estimation of the law of motion of flight vehicles from the results of the trajectory measurements, as well as the methods and algorithms of minimax estimation under fixed sets of uncertainty of the covariance matrices of the observation errors. A minimax-statistical approach to the problem of estimation on the basis of uncertainty sets in the form of confidence regions of given reliability was proposed. The results of numerical modeling were presented.     

Minimax linear filtering of random sequences with uncertain covariance function

Consideration was given to the development of a numerical method for determination of the minimax filter in the linear stochastic difference system studied over a finite horizon in the presence of an uncertain covariance function in the model of useful signal. Selection of the considered uncertainty sets relied on the form of the corresponding confidence regions. The developed iterative procedure was applied to filtering of the position of a maneuvering target with inexactly given acceleration covariance function.     

Minimax filtering in linear stochastic uncertain discrete-continuous systems

Filtering of the states of a system, whose dynamics is defined by an Ito stochastic differential equation, by discrete and discrete-continuous observations is studied under the assumption that the intensities of continuous noises and covariance matrices of discrete noises are known only within to membership of certain uncertainty sets. A minimax approach is used to solve the problem. The filter is optimized with an integral quality criterion. Minimax filtering equations are derived from the solution of the dual optimization problem. A numerical solution algorithm for the problem is designed. Results of numerical experiments are presented.     

Robust tracking problem for continuous time stochastic uncertain systems

We propose a finite‐horizon robust minimax tracking controller design method for time‐varying continuous time stochastic uncertain systems. The uncertainty in the system is characterized by a set of probability measures under which stochastic noises, driving the system, are defined. A minimax optimal tracking controller is derived from the solution of a risk‐sensitive linear quadratic Gaussian control problem. Also a numerical example is presented to illustrate the characteristics of proposed tracking controller. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

求解支付值为区间直觉模糊数的矩阵对策的线性规划方法

提出了支付值为区间直觉模糊集的矩阵对策定义及其解的概念, 将求解局中人的极大-极小与极小-极大策略问题转化为求解一对辅助的非线性多目标规划, 进而转化为一对易于求解的原始-对偶线性规划. 数值实例表明了所提方法的有效性和实用性. 所提出的区间直觉模糊集矩阵对策理论与方法既是对经典矩阵对策理论的发展, 又可为解决其他带有区间直觉模糊信息的对策问题提供新的途径.

     

一类非线性极小极大问题的改进粒子群算法

针对一类非线性极小极大问题目标函数非光滑的特点给求解带来的困难,利用改进的粒子群算法并结合极大熵函数法给出了此类问题的一种新的有效算法。首先利用极大熵函数将无约束和有约束极小极大问题转化为一个光滑函数的无约束最优化问题,将此光滑函数作为粒子群算法的适应值函数;然后用数学中的外推方法给出一个新的粒子位置更新公式,并应用这个改进的粒子群算法来优化此问题。数值结果表明,该算法收敛快﹑数值稳定性好,是求解非线性极小极大问题的一种有效算法。     

枢纽成本约束下的零担物流轴辐式网络设计

针对考虑枢纽建造成本和货物流的不确定的枢纽新建方案问题,引入全寿命周期理论,建立以轴辐式运营网络总成本最小化为目标的混合整数线性规划模型,并提出改进的最大最小后悔值的不确定性决策方法。通过算例来分析投资年限、枢纽干线折扣系数和不确定枢纽建造成本对零担物流（LTL）轴辐式网络的设计的影响。实验结果表明, 采用改进的不确定性决策方法得到的最优方案的运营成本比5个场景的运营成本平均降低了2.17%,表明基于改进的最大最小后悔值的不确定性决策方法,能够降低整个零担物流轴辐式运营网络总成本。     

The Wonham filter under uncertainty: A game–theoretic approach

The paper is devoted to a state filtering problem of Markov jump processes given the continuous and/or counting observations. All the transition intensity matrix, observation plan and counting intensity are parameterized by a random vector with uncertain distribution on a known support set.The estimation problem is formulated in minimax settings with a conditional optimality criterion. We reduce the initial minimax problem to a dual problem of constrained quadratic optimization. The corresponding numerical algorithm of minimax filtering is presented as well as its illustrative implementation in the monitoring of a TCP link status under uncertainty.     

On the estimation of uncertain signals using an estimation-detection scheme

An estimation-detection scheme is proposed for the estimation of signals with unknown parameters. The scheme is illustrated by considering the case of a signal with unknown but bounded variance. Two sets of bounds are used to determine two regions of signal uncertainty, and different linear estimators are used for each region. A detector is then used to select the appropriate estimator. Since no a priori information is assumed on the unknown parameters a weighted minimax criterion is used to optimize the detector and the estimators.     

一类广义时滞系统的极小极大控制

研究一类广义时滞系统的极小极大控制问题,目的是利用构造局部检验函数的方法设计极小极大控制器,使得在最坏的干扰下系统的性能指标上界极小.利用线性矩阵不等式(LM I)给出了广义时滞系统极小极大控制器存在的充分条件,讨论了闭环系统的容许性,并将所得结果推广到含有不确定性的广义时滞系统.最后以数值算例说明了所提出的控制器设计方法的有效性和可行性.     

基于粗糙熵权的模糊多准则决策方法及应用

提出度量粗糙集不确定性的粗糙熵概念,并基于粗糙熵对属性的重要度给出了定义,从而提供一种求解模糊多准则决策模型中准则权重的方法．根据备选方案的模糊评价系统,给出相关的知识表达系统,并建立删除冗余属性后的备选方案综合评价优选矩阵,通过比较各备选方案与理想方案的近似度求出最优方案．最后,通过实例计算证明了上述模型及方法的有效性．     

Uncertainty modeling and robust minimax LQR control of multivariable nonlinear systems with application to hypersonic flight

For a class of multi‐input and multi‐output nonlinear uncertainty systems, a novel approach to design a nonlinear controller using minimax linear quadratic regulator (LQR) control is proposed. The proposed method combines a feedback linearization method with the robust minimax LQR approach in the presence of time‐varying uncertain parameters. The uncertainties, which are assumed to satisfy a certain integral quadratic constraint condition, do not necessarily satisfy a generalized matching condition. The procedure consists of feedback linearization of the nominal model and linearization of the remaining nonlinear uncertain terms with respect to each individual uncertainty at a local operating point. This two‐stage linearization process, followed by a robust minimax LQR control design, provides a robustly stable closed loop system. To demonstrate the effectiveness of the proposed approach, an application study is provided for a flight control problem of an air‐breathing hypersonic flight vehicle (AHFV), where the outputs to be controlled are the longitudinal velocity and altitude, and the control variables are the throttle setting and elevator deflection. The proposed method is used to derive a linearized uncertainty model for the longitudinal motion dynamics of the AHFV first, and then a robust minimax LQR controller is designed, which is based on this uncertainty model. The controller is synthesized considering seven uncertain aerodynamic and inertial parameters. The stability and performance of the synthesized controller is evaluated numerically via single scenario simulations for particular cruise conditions as well as a Monte‐Carlo type simulation based on numerous cases. It is observed that the control scheme proposed in this paper performs better, especially from the aspect of robustness to large ranges of uncertainties, than some controller design schemes previously published in the literature. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

F-邻域粗糙集及其约简

邻域粗糙集可以直接处理数值型数据,F-粗糙集是第一个动态粗糙集模型.针对动态变化的数值型数据,结合邻域粗糙集和F-粗糙集的优势,提出了F-邻域粗糙集和F-邻域并行约简.首先,定义了F-邻域粗糙集上下近似、边界区域;其次,在F-邻域粗糙集中提出了F-属性依赖度和属性重要度矩阵;根据F-属性依赖度和属性重要度矩阵分别提出了...     

Robust filtering of process in the stationary difference stochastic system

A technique to construct the robust Kalman filter for process estimation in the difference linear stationary stochastic system with an unknown covariance observation error matrix was developed. Consideration was given to the algorithm of constructing the set of permissible covariance matrices from a priori statistical data. A numerical method for solution of the general minimax optimization problem was proposed; and on its basis an iterative algorithm to calculate the robust filter parameters was developed, and its convergence was proved. Results of the numerical experiment were presented.     

基于粗糙集的数据聚类方法研究

RoughSets理论是一种新型的处理含糊和不确定性知识的数学工具,将RoughSets理论应用于知识发现中的聚类分析,给出了信息系统的约简、信息系统的可辨识属性矩阵和信息系统的辨识公式等定义,在此基础上提出了基于粗糙集的数据聚类算法RSDC,实验结果验证了该算法的可行性,并且对符号属性和数值属性数据都具有良好的聚类效果。     

On the worst-case disturbance of minimax optimal control

We consider a minimax optimal control problem for uncertain stochastic systems. The uncertainty in the underlying stochastic system is formulated in terms of probability measure perturbations satisfying a relative entropy constraint. By characterizing the worst-case measure for a related stochastic minimax game, it is shown that the worst-case uncertain system can be represented in the form of a parametric perturbation of the nominal system. A numerical example is presented to illustrate theoretical results developed in this paper.     

Planning for dynamic multiagent planar manipulation with uncertainty: a game theoretic approach

This paper addresses the planning problem for multiagent dynamic manipulation in the plane. The objective of planning is to design the forces exerted on the object by agents with which the object can follow a given trajectory in spite of the uncertainty on pressure distribution. The main novelty of the proposed approach is the integration of noncooperative and cooperative games between agents in an hierarchical manner. Based on a dynamic model of the pushed object, the coordination problem is solved in two levels. In the lower control level, a fictitious force controller is designed by using a minimax technique to achieve the tracking performance. The design procedure is divided into two steps. First, a linear nominal controller is designed via full-state linearization with desired eigenvalues assignment. Next, a minimax control scheme is specified to optimally attenuate the worst-case effect of the uncertainty due to pressure distribution and achieve a minimax tracking performance. In the coordination level, a cooperative game is formulated between agents to distribute the fictitious force, and the objective of the game is to minimize the worst-case interaction force between agents and the object. Simulations are carried out for two-agent and three-agent manipulations, results demonstrate the effectiveness of the planning method.     

Minimax control of switching systems under sampling

We consider a general class of systems subject to two types of uncertainty: A continuous deterministic uncertainty that affects the system dynamics, and a discrete stochastic uncertainty that leads to jumps in the system structure at random times, with the latter described by a continuous-time finite state Markov chain. When only sampled values of the system state is available to the controller, along with perfect measurements on the state of the Markov chain, we obtain a characterization of minimax controllers, which involves the solutions of two finite sets of coupled PDEs, and a finite dimensional compensator. For the linear-quadratic case, a complete characterization is given in terms of coupled generalized Riccati equations, which also provides the solution to a particular H^∞ optimal control problem with randomly switching system structure and sampled state measurements.     

On the design of suboptimal sliding manifold for a class of nonlinear uncertain time-delay systems

This paper proposes a new method to design suboptimal sliding manifolds for a class of nonlinear uncertain systems with state and input delays. A switching control law is obtained based on the designed suboptimal sliding manifold. It is proved that the proposed method is able to guarantee the stability of the closed-loop system in the presence of uncertainty. Three numerical simulations are given to illustrate the effectiveness of the proposed method.     

基于软粗糙集的犹豫模糊三支决策方法

三支决策理论采取“三分而治”的处理思路,为复杂问题求解提供了一种简洁高效的解决方案.对此,借助软集理论研究犹豫模糊集和三支决策方法,通过定义犹豫模糊集的值空间和值陪集,引入犹豫模糊集的典范软集、单位区间参数化软集和导出犹豫模糊集等概念,解决犹豫模糊集和软集的相互表示问题.此外,利用软粗糙集理论建立一种基于犹豫模糊集的广义粗糙模型,借助给定的预决策集,计算软上近似集并确定评价函数,进而提出一种基于软粗糙集的犹豫模糊三支决策方法.最后,通过两个数值实例和相关对比分析,验证所提出三支决策方法的合理性和有效性.