基于模糊-粗糙集算法的知识级约束建模

提出了支持复杂产品并行协同设计的广义动态约束网络(GDCN)概念和模型;然后,提出了模糊－粗糙集算法,对仿真结果进行数据挖掘,实现知识级约束获取;最后,开发了基于仿真的知识级约束获取系统,并以某型号V6发动机曲柄连杆机构设计为例,说明了方法的准确性和有效性。     

鲁棒并行设计的模型与算法

提出一种基于约束网络的鲁棒设计方法,该方法能用约束网络对并行设计中的一类不确定性参数进行有效的描述和处理。提出了反映该问题需求的数学模型,设计了通用的一致性求解算法框架,得到了满足该框架的算法都是弧一致的充分条件以及推论,并利用区间算法对该框架进行实现。飞行器布局设计实例说明了该方法的有效性。     

面向约束的鲁棒设计方法

提出了一种面向约束的鲁棒设计方法,该方法能够利用约束网络对并行设计中的一类不确定性参数进行有效的描述和处理。该文提出了反映协同设计需求的数学模型,设计了一个通用的一致性求解算法框架,并利用基于区间代数的一致性算法,在设计的初期就监控设计过程,使设计者在确定参数前能够通过约束网络定量地考虑下游的约束条件,并能够提早发现冲突以避免设计后期出现大的返工。文中的研究是实现并行设计中“一次成功”的技术基础。     

基于参数协调模型的多学科协同设计方法

采用传统方法解决多学科的设计问题时 ,因为无法定量考虑分布在多个学科内的产品指标对设计变量的影响 ,通常会产生频繁的设计返工 .该文通过收集产品各领域的指标约束与关系约束来构建基于约束网络的产品参数协调模型 ,采用区间描述设计变量的不确定性信息 ,并将区间算法与基因算法相结合实现了一致性模型求解框架 .使用该方法能够检测潜在冲突 ,得到与给定产品指标相对应的设计变量可行解空间 ,进而辅助设计人员决策 .最后以该方法在转向架设计中的应用实例说明其有效性 .     

基于关联约束非二元弧一致性的约束满足问题求解

弧一致性算法在二元约束满足问题中取得了成功的应用,但并不能被有效泛化至预处理非二元约束满足问题(NCSP).本文提出了处理NCSP的关联约束非二元弧一致性算法.通过随机NCSP生成器产生问题实例,分别采用关联约束非二元弧一致性算法和非二元弧一致性算法进行预处理,并对预处理后的问题实例应用回溯算法进行求解.对比分析采用两种预处理算法和不采用预处理下回溯算法的求解性能,仿真实验结果表明关联约束非二元弧一致性算法可以有效地剔除冗余的约束元组和变量域值,使关联约束非二元弧一致性回溯算法具有更良好的鲁棒性.     

炼钢机器故障下炼钢-连铸调度DCSP建模与算法

针对机器故障下的炼钢-连铸动态调度问题,基于动态约束满足技术开发了能够灵活反映各种动态因素的建模机制。从变量、值域和约束三个角度将生产过程中的机器故障的影响映射为约束满足模型的动态变化;提出了重调度前后调度方案在时间安排和机器指派上的一致性度量方法,以满足不同炉次对时间和机器一致性的不同要求;将机器故障扰动按影响程度分为3个层级,建立了故障扰动与约束满足调度模型间的映射关系。根据炼钢、精炼阶段的机器故障扰动程度,制定不同的求解策略,并为机器指派变量赋值;基于约束传播技术,通过调整开工时间和柔性加工时间分步消解时间约束冲突。仿真实验表明,提出的模型和算法是可行和有效的。     

基于约束的调度研究和实现

运用约束程序设计(CP)思想和技术来调度正成为一个新兴的研究领域。文章首先对CP和调度的相关领域知识进行了简要介绍;然后按照CP所倡导的问题建模和问题求解相分离的思想,建立起一般理论调度问题的约束模型,并设计实现了一个基于约束的调度求解算法CBS-1;并对一些典型问题进行了实验,实验结果表明算法提高了约束调度求解的效率和通用性。     

一种基于时间戳的简单表缩减算法

表约束是一种外延的知识表示方法,每个约束在对应的变量集上列举出所有支持或禁止的元组.广义弧相容（generalized arc consistency,简称GAC）是求解约束满足问题应用最广泛的相容性.Simple Tabular Reduction（STR）是一类高效的维持GAC的算法.在回溯搜索中,STR动态地删除无效元组,降低了查找支持的开销,并拥有单位时间的回溯代价,在高元表约束上获得了广泛运用,并有大量基于STR的改进算法被提出,其中,元组集的压缩表示是目前研究较多的方法.同样基于动态维持元组集有效部分的思想,为STR提出一种检测并删除无效元组和为变量更新支持的算法,作用于原始表约束并拥有单位时间的回溯代价.实验结果表明,该算法在表约束上维持GAC的效率普遍高于现有的非基于压缩表示的STR算法,并且在一些实例上的效率高于最新的基于元组集压缩表示的STR算法.     

基于扩展影响图的超视距空战辅助决策方法

利用扩展影响图的表示特性和计算特性来解决辅助决策系统中知识表示与问题求解的一致性问题.采用条件弧和决策簇扩展影响图解决其在描述非对称性、不确定性问题中的局限,并根据该扩展影响图提出了基于条件分解的求解算法.基于扩展影响图方法对系统进行分析并描述系统结构,给出了基于扩展影响图进行辅助任务分析设计的框架和系统结构.仿真结果表明了所提出方法的有效性.     

面向复杂作业车间的交互式两级调度方法

针对边界不确定和具有决策偏好的大规模复杂作业车间调度问题,提出以第1级为交互式约束设置求解,第2级为优化求解的两级调度求解策略.在第1级调度中研究交互式约束满足的基于优先级快速调度构建算法,作为支持决策者交互式约束调整的快速响应求解方法.在第2级调度中以基于优先级的快速调度构建算法为基础,研究以优先级为决策变量的智能优化搜索算法,作为满足第1级调度中的交互式约束的改进优化求解.该方法较好地融入了决策者的经验知识和偏好,同时结合优化搜索求解算法,使得在满足决策者偏好的基础上进一步改进调度求解质量,增加调度求解结果的可信度,在实际应用中取得良好的应用效果.最后,通过一个案例对该两级调度求解策略的过程进行描述,并对所提出方法的有效性进行阐述.     

Collaborative robust optimization under uncertainty based on generalized dynamic constraints network

Uncertainties exist in every aspect of a collaborative multidisciplinary design process. These uncertainties will have a great influence on design negotiations between various disciplines and may force designers to make conservative decisions. In this paper, a novel collaborative robust optimization (CRO) method based on constraints network under uncertainty is presented. The generalized dynamic constraints network (GDCN) is developed to analysis and management of uncertainties, and to ensure the parameter consistency in the collaborative design process. Given the feasible consistent parameter region, The CRO is formulated as a multi-criteria optimization problem, which brings both the objective robustness and the feasibility robustness of the constraint into account simultaneously. The CRO based on GDCN could bring both the design parameters dynamic consistency management and robust optimization into account simultaneously, which assures a product’s reliability and quality robustness. The efficiency of proposed method is evaluated in the design of crank and connecting rod in one V6 engine.     

Bounded robust control of nonlinear systems using neural network�Cbased HJB solution

In this paper, a Hamilton–Jacobi–Bellman (HJB) equation–based optimal control algorithm for robust controller design is proposed for nonlinear systems. The HJB equation is formulated using a suitable nonquadratic term in the performance functional to tackle constraints on the control input. Utilizing the direct method of Lyapunov stability, the controller is shown to be optimal with respect to a cost functional, which includes penalty on the control effort and the maximum bound on system uncertainty. The bounded controller requires the knowledge of the upper bound of system uncertainty. In the proposed algorithm, neural network is used to approximate the solution of HJB equation using least squares method. Proposed algorithm has been applied on the nonlinear system with matched and unmatched type system uncertainties and uncertainties in the input matrix. Necessary theoretical and simulation results are presented to validate proposed algorithm.     

VHDL延时语句的综合算法研究

为了使得综合系统能够自动综合经设计迭代后由反向标注得到的时序信息,同时提高综合结果与模拟结果的时序一致性,对作为时序信息载体的延时语句的综合方法进行了研究,将延时语句考虑为延时约束,并提出了相应调度模型DTC_DFG及其调度算法。采用启发式机制使得调度算法的解空间搜索过程具有跳出局部最优的能力,此算法可在多项式时间复杂度下得到全局最优调度解。实验结果表明,该制度算法不仅能够有效综合延时语句,使得综合结果能够与模拟结果达到较好的一致,而且提供了一种给出延时约束的方便手段,减少了综合过程中的人工干预,极大地提高了设计效率。     

基于约束的CPM动态优化算法

利用基于蒙特卡罗模拟的关键路径(CPM)优化算法模型,通过分析项目计划中各任务之间可能存在工序约束和期限约束对CPM的影响,提出了基于约束的CPM动态优化算法.该算法可得到CPM,工期及其统计特性和相关的冲突信息,为决策者提供更好的决策信息支持.采用Matlab,C#和SQL Server实现了所提出算法,实例分析验证了该算法的有效性.     

装配配合约束自动获取技术研究

装配配合约束获取对于装配设计、装配工艺规划等多个领域的研究具有重要意义;现有的装配配合约束获取方法容易产生约束的遗漏和约束无效性;通过对自动获取配合约束技术进行研究,在充分利用CAD模型几何拓扑信息的基础上。提出了几何约束识别和配合验证相结合的装配配合约束自动获取箅法;实践证明,该方法可以显著地提高约束信息的获取效率、正确性和有效性。     

Optimal residual design for fault diagnosis using multi-objective optimization and genetic algorithms

This paper develops a new approach to the design of optimal residuals in order to diagnose incipient faults based on multi-objective optimization and genetic algorithms. In this approach the residual is generated via an observer. To reduce false and missed alarm rates in fault diagnosis, a number of performance indices are introduced into the observer design. Some performance indices are expressed in the frequency domain to take account of the frequency distributions of faults, noise and modelling uncertainties. All objectives then are reformulated into a set of inequality constraints on performance indices. A genetic algorithm is thus used to search for an optimal solution to satisfy these inequality constraints on performance indices. The approach developed is applied to a flight control system example, and simulation results show that incipient sensor faults can be detected reliably in the presence of modelling uncertainty.     

The challenge of equality constraints in robust design optimization: examination and new approach

In recent years, robust design optimization (RDO) has emerged as a significant area of research. The focus of RDO is to obtain a design that minimizes the effects of uncertainty on product reliability and performance. The effectiveness of the resulting solution in RDO highly depends on how the objective function and the constraints are formulated to account for uncertainties. Inequality constraint and objective function formulations under uncertainty have been studied extensively in the literature. However, the approaches for formulating equality constraints in the RDO literature are in a state of disharmony. Moreover, we observe that these approaches are generally applicable only to certain special cases of equality constraints. There is a need for a systematic approach for handling equality constraints in RDO, which is the motivation for this research. In this paper, we examine critical issues pertinent to formulating equality constraints in RDO. Equality constraints in RDO can be classified as belonging to two classes: (1) those that cannot be satisfied, because of the uncertainty inherently present in the RDO problem, and (2) those that must be satisfied, regardless of the uncertainty present in the problem. In this paper, we propose a linearization- based approach to classify equality constraints into the above two classes, and propose respective formulation methods. The theoretical developments presented in this paper are illustrated with the help of two numerical examples.     

Adaptive Neural Network-Based Control for a Class of Nonlinear Pure-Feedback Systems With Time-Varying Full State Constraints

In this paper, an adaptive neural network (NN) control approach is proposed for nonlinear pure-feedback systems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions (BLFs) with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closedloop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach.      

通信受限的网络化多智能体系统编队控制

主要研究了具有通信受限及不确定性的无线网络下的二阶多智能体系统的编队控制问题.针对无线网络的介质访问约束,采用二进制序列设计智能体节点调度协议,使得满足约束条件的智能体节点经网络传输其采样信息;另一方面,为建立更符合实际的通信信道模型,区别于传统的固定连接权重拓扑图,考虑到无线网络的不确定性及拓扑连接权重与智能体节点之间距离的关系,利用无线信道的度量指标构建了多智能体通信拓扑的新模型.定义信息更新误差及编队误差,将二阶多智能体系统模型转化为闭环延时系统模型.然后,构造Lyapunov-Krasovskii函数并利用LMI方法设计了编队控制协议.最后,通过仿真验证了所提方法的有效性.