采用鲁棒容许集对有约束的不确定系统作鲁棒模型预测控制

针对输入和状态受约束的干扰有界多胞不确定线性系统,提出了基于鲁棒容许集的扩大吸引域鲁棒模型预测控制(RMPC)方法.首先给出了多面体不变集的鲁棒容许集计算方法,并推导了鲁棒容许集存在的充分必要条件.其次,为了拓展Tube不变集鲁棒模型预测控制算法的适用范围,讨论了干扰有界多胞不确定线性系统的Tube不变集控制策略.之后为了扩大约束系统吸引域,提出了干扰有界多胞不确定系统的鲁棒容许集模型预测控制策略.通过采用鲁棒容许集和Tube不变集RMPC,该方法不仅扩大了吸引域,而且降低了在线计算量;同时,采用基于最小鲁棒正不变集的Tube不变集策略保证了算法的鲁棒性.最后仿真结果验证了算法的有效性.     

不确定旅行商问题的鲁棒模型与算法

考虑到不确定参数在旅行商问题(TSP)中广泛存在,在Bertsimas鲁棒离散优化理论的框架下,建立了不确定旅行商问题的鲁棒优化模型,并按转换规则将鲁棒模型转换为鲁棒对等模型。给出了一种求解旅行商问题的基于Prufer数编码的单亲遗传算法,与求解该类问题的传统遗传算法相比,该算法缩减了染色体长度,避免了传统交叉和变异操作破坏染色体可行解的缺陷。通过算例验证,表明该算法有较高的求解效率,所建立的鲁棒模型在不确定环境下能得到较好的鲁棒解。     

不确定条件下控制分配问题的鲁棒优化方法

针对具有冗余执行机构的过驱动系统, 在考虑控制效率不确定性的条件下, 提出了一种基于鲁棒优化理论的控制分配算法. 研究了原始不确定鲁棒优化模型的建立和基于椭球不确定集的鲁棒对等式的转化问题, 并推广到可由锥二次不等式表示的不确定集的情况. 讨论了鲁棒优化控制分配算法的求解方法及其计算复杂度. 最后, 针对多操纵面飞机的最优控制分配问题与传统算法进行了仿真比较, 结果表明鲁棒优化算法能有效降低控制效率不确定性的影响, 使分配结果更为合理, 从而具有更好的鲁棒性, 同时能有效提高操纵面故障情况下闭环系统的控制重构能力, 很好地改善了飞控系统的性能.     

时滞和参数不确定的供应链动态模型及其鲁棒H∞控制

研究了不确定环境下的供应链运作问题，并建立了具有生产时滞、成本参数和需求不确定性的供应链动态模型．分析了供应链的鲁棒运作，采用鲁棒H∞控制策略和线性矩阵不等式（LMI）算法处理供应链系统鲁棒运作问题．借助供应链库存状态的静态反馈控制，使供应链动态系统达到抑制不确定性干扰的作用，并使供应链运作达到理想总成本．最后，通过仿真计算验证了所得结果．     

互质因子摄动模型集的闭环l1鲁棒辨识

鲁棒辨识问题已经逐渐成为国际控制界的一个研究热点，本文首先研究了互质因子摄动模型集的闭环ｌ＾１鲁棒辨识问题，建立了系统的Ａ图象拓扑，提出了ＢＩＢＯ不稳定系统的闭环ｌ＾１鲁棒辨识方法，由该方法得到的结果是具体代表性的互质因子摄动模型集。     

应急物流车辆调度多目标鲁棒优化研究

针对应急物流车辆调度问题中对于经济性、时效性、可靠性和鲁棒性的多种要求，考虑了含有时间窗、不确定需求、不确定行驶时间，以及路段含有失效风险的多目标鲁棒车辆路径优化问题，通过定义新的成本函数、满意度函数、风险度函数和鲁棒度函数作为四个优化目标来构建模型，并基于鲁棒优化理论将不确定模型转化为确定性鲁棒对应模型求解，为解决不确定环境下优化问题提供了新的思路。算法方面，主要基于SPEA2算法框架求解该多目标模型，针对算法缺陷提出多种改进策略，并通过对比实验证明了改进策略的有效性。     

最坏情况下l^1鲁棒辨识方法研究

系统地讨论了ＳＩＳＯ、线性时不变、指数稳定系统在最坏情况下的ｌ＾１鲁棒辨识问题。提出了系统模型集合的最小外框概念，建立了两种任意非零信号作用下ｌ＾１鲁棒辨识算法；提出了任意非零信号作用下系统的可辨识条件；证明了算法的全局收敛性和最优性。     

鲁棒（Robust）谱估计算法研究

本文在估计理论,系统辨识,B. D. Martin D. Thomson工作的基础上,提出一种改进的鲁棒谱估计算法。该方法易于直接用于在线鲁棒谱估计。文中给出仿真实例。     

动态系统的鲁棒容错控制方法

本文针对有参数不确定性的动态系统设计了一种参数鲁棒故障检测器及故障向量辨识 器,提出了状态向量和输出向量的校正方法.最后给出应用所提算法对飞机实现鲁棒容错控 制的例子,数字仿真试验表明效果良好.     

非线性系统神经网络辨识的鲁棒BP算法

讨论系统辨识神经网络算法的鲁棒性问题。通过构造新的动态鲁棒目标函数得到的ＲＢＰ算法，能不断估计逼近精度，自动将品质好的样本置于强化学习域，并能有效地抵抗噪声干扰。实验结果表明，该算法具有鲁棒性强、收敛快、计算方便等特点。     

离散系统状态和参数鲁棒滤波分离算法

提出了一种离散系统的鲁棒分离滤波方法.为了对状态向量进行较准确估计,将鲁棒滤波器分为:1)零误差状态估计器;2)不确定矩阵估计器;3)鲁棒合成器.零偏差状态估计器是假定系统的不确定部分为零时的状态估计器;其新息作为不确定部分的估计变量,并由此估计系统的不确定部分;最后,根据系统不确定部分估计误差的上下界,用鲁棒合成器对状态向量的估计值进行鲁棒修正.为了在合成器中得到鲁棒滤波的逼近计算式,通过变换状态估计误差的协方差阵,得到了系统矩阵不确定部分的误差上界不等式逼近,并且得到了估计误差协方差阵逆阵的下界不等式逼近,从而给出了鲁棒合成滤波的完整算法.     

Robust identification for multi-section freeway traffic models

1IntroductionIt is important to estimate the densityandspeed oftrafficfor the safetyandtraffic control .For decades ,manyresearchwork have been done to estimate traffic density, trafficvolume ,average speed,and other parameters[1,2] .Theproblemof estimating dynamic traffic has been involved inparts of those research work[1 ~4] .By means of O_Dmatrix,some researchers have also made a series of studiesof traffic prediction and traffic layout estimation[5] .However , most of the research work m…     

Robust identification for multi_section freeway traffic models

Since it is difficult to fit measured parameters using the conventional traffic model, a new traffic density and average speed model is introduced in this paper.To determine traffic model structures accurately, a model identification method for uncertain nonlinear system is developed.To simplify uncertain nonlinear problem, this paper presents a new robust criterion to identify the multi-section traffic model structure of freeway efficiently.In the new model identification criterion,numerically efficient U-D factorization is used to avoid computing the determinant values of two complex matrices.By estimating the values of U-D factor of data matrix, both the upper and lower bounds of system uncertainties are described. Thus a model structure identification algorithm is proposed.Comparisons between identification outputs and simulation outputs of traffic states show that the traffic states can be accurately predicted by means of the new traffic models and the structure identification criterion.     

Robust identification method for nonlinear model structures and its application to high-performance aircraft

A common assumption is that the model structure is known for modelling high performance aircraft. In practice, this is not the case. Actually, structure identification plays the most important role in the processing of nonlinear system modelling. The integration of mode structure identification and parameter estimation is an efficient method to construct the model for high performance aircraft, which is nonlinear and also contains uncertainties. This article presents an efficient method for identifying nonlinear model structure and estimating parameters for high-performance aircraft model, which contains uncertainties. The parameters associated with nonlinear terms are considered one after the other if they should be included in the nonlinear model until a stopping criterion is met, which is based on Akaike's information criterion. A numerically efficient U-D factorisation is presented to avoid complex computation of high-order matrices. The proposed method is applied to flight test data of a high-performance aircraft. The results demonstrate that the proposed method could obtain the good aircraft model with a reasonably good fidelity based on the comparison with flight test data.     

Stability of uncertain impulsive stochastic fuzzy neural networks with two additive time delays in the leakage term

This paper is concerned with the stability problem for a class of impulsive neural networks model, which includes simultaneously parameter uncertainties, stochastic disturbances and two additive time-varying delays in the leakage term. By constructing a suitable Lyapunov–Krasovskii functional that uses the information on the lower and upper bound of the delay sufficiently, a delay-dependent stability criterion is derived by using the free-weighting matrices method for such Takagi–Sugeno fuzzy uncertain impulsive stochastic recurrent neural networks. The obtained conditions are expressed with linear matrix inequalities (LMIs) whose feasibility can be checked easily by MATLAB LMI Control toolbox. Finally, the theoretical result is validated by simulations.     

具有噪声方差及多种网络诱导不确定系统鲁棒Kalman估计

对不确定噪声方差乘性噪声,同时带观测缺失、丢包和一步随机观测滞后三种网络诱导特征的混合不确定网络化系统,应用带虚拟噪声的扩维方法和去随机参数方法,将其转化为带不确定虚拟噪声方差的时变系统.基于极大极小鲁棒估计原理,对带虚拟噪声方差保守上界的最坏情形系统,设计了鲁棒时变和稳态Kalman估值器.对所有容许的不确定性,保证实际Kalman估计误差方差有最小上界.应用扩展的Lyapunov方程方法和矩阵分解方法证明了所设计估值器的鲁棒性.证明了实际和保守估值器的精度关系,以及时变和稳态估值器间的按实现收敛性.应用于F-404航空发动机系统的仿真验证了所提出结果的正确性和有效性.     

Identification of dynamical systems with a robust interval fuzzy model

In this paper we present a new method of interval fuzzy model identification. The method combines a fuzzy identification methodology with some ideas from linear programming theory. On a finite set of measured data, an optimality criterion that minimizes the maximal estimation error between the data and the proposed fuzzy model output is used. The idea is then extended to modelling the optimal lower and upper bound functions that define the band that contains all the measurement values. This results in a lower and an upper fuzzy model or a fuzzy model with a set of lower and upper parameters. The model is called the interval fuzzy model (INFUMO). The method can be used when describing a family of uncertain nonlinear functions or when the systems with uncertain physical parameters are observed. We believe that the fuzzy interval model can be very efficiently used, especially in fault detection and in robust control design.     

区间系统的离散鲁棒Kalman滤波

着重研究区间系统的鲁棒滤波问题.通过等价变换,将区间系统转换为对应的矩阵 范数不确定系统,并据此导出区间系统的鲁棒滤波算法.该算法为一离散验后(a posteriori) 滤波算法,它能保证区间系统的滤波误差有界.理论分析和实际计算结果表明,本算法能达到 比验前(a priori)滤波小得多的滤波误差的方差上界.     

Multivariable process identification for mpc: the asymptotic method and its applications

In this work we will introduce the asymptotic method (ASYM) of identification and provide two case studies. The ASYM was developed for multivariable process identification for model based control. The method calculates time domain parametric models using frequency domain criterion. Fundamental problems, such as test signal design for control, model order/structure selection, parameter estimation and model error quantification, are solved in a systematic manner. The method can supply not only input/output model and unmeasured disturbance model which are asymptotic maximum likelihood estimates, but also the upper bound matrix for the model errors that can be used for model validation and robustness analysis. To demonstrate the use of the method for model predictive control (MPC), the identification of a Shell benchmark process (a simulated distillation column) and an industrial application to a crude unit atmospheric tower will be presented.     

考虑不确定测量和个体差异的非线性随机退化系统剩余寿命估计

剩余寿命估计是预测与健康管理的基础,是降低系统运行风险、提高系统安全性与可靠性的有效途径.针对工程实际中大量存在的非线性随机性退化系统,现有方法仅单独考虑了不确定测量或系统间个体差异对剩余寿命的影响,尚未实现同时考虑不确定测量和个体差异的剩余寿命估计.因此,本文首先建立了一种基于扩散过程的非线性退化模型,进一步通过建立的状态空间模型和Kalman滤波实现了同时考虑不确定测量和个体差异下的随机退化系统剩余寿命自适应估计,同时对漂移系数进行自适应估计,以获取非线性退化系统更加精确的剩余寿命估计.最后,将所提方法应用于疲劳裂纹和陀螺仪的监测数据,结果表明本文方法显著优于仅考虑不确定测量或仅考虑个体差异的寿命估计方法,具有潜在的工程应用价值.