On the filtered Smith predictor for MIMO processes with multiple time delays

This paper discusses about a unified implementation structure of the filtered Smith predictor (FSP) for MIMO processes with multiple time delays. Two kinds of dead-time free models are analyzed in order to extend the original properties of the SISO Smith predictor to MIMO processes with multiple time delays. It is demonstrated that FSP strategy can be applied to control open-loop unstable processes with multiple time delays by considering a model without any input, output or internal coupling delays. Moreover, for open-loop stable processes, it is shown that the FSP scheme can be used to speed-up disturbance rejection of processes with multiple time delays by using a nominal model without delay. Different simulation examples are used to illustrate the proposed strategy properties and advantages over other MIMO time-delay compensators. The results may be considered to control either square or non-square processes.     

双层模糊系统融合中心约束型最小包含球

与传统的TSK模糊系统相比,改进的双层TSK模糊系统CTSK(Central TSK Fuzzy System)有如下优点:良好的可解释性、更好的鲁棒性、较强的逼近能力。但对于大样本或超大样本数据集,其时间复杂度和空间复杂度的开销都极大地限制了它的实用性。针对此不足,通过模糊系统融合中心约束型最小包含球(CCMEB)理论提出了CCMEB-CTSK(CCMEB-based CTSK)算法。该算法在继承CTSK优点的同时,又较好地实现了处理大样本和超大样本数据集的有效性和快速性。仿真实验研究分析了采用不同模糊规则数的CCMEB-CTSK的性能指标和运行时间的比较,以及训练样本不加噪声和加入噪声情况下CCMEB-CTSK泛化能力和鲁棒性能的测试。     

Introducing robustness in model predictive control with multiple modelsand switching

Model predictive control is model-based. Therefore, the procedure is inherently not robust to modelling uncertainties. Further, a crucial design parameter is the prediction horizon. Only offline procedures to estimate an upper bound of the optimal value of this parameter are available. These procedures are computationally intensive and model-based. Besides, a single choice of this horizon is perhaps not the best option at all time instants. This is especially true when the control objective is to track desired trajectories. In this paper, we resolve the issue by a time-varying horizon achieved by switching between multiple model-predictive controllers. The stability of the overall system is discussed. In addition, an introduction of multiple models to handle modelling uncertainties makes the overall system robust. The improvement in performance is demonstrated through several examples.     

跟踪微分器在生物雷达中的应用

在探测废墟下存活的人体时,生物雷达很难找到理想的参考信号对采集的信号进行处理.对已有生物雷达信号处理方法性能分析,提出了基于微分跟踪器（TD）的信号处理方法.该方法不需要理想的信号作为参考信号,且收敛性能和鲁棒性好、稳态误差小.MATLAB仿真分析证明了该算法应用于生物雷达信号处理的有效性.     

结构不确定Delta算子系统的鲁棒容错镇定

研究了圆形区域极点约束下Delta算子描述的不确定线性系统的鲁棒容错镇定问题.基于Riccati方程,导出了当Delta算子系统存在不确定性和执行器故障时,将闭环系统极点配置到指定圆盘内,确保系统鲁棒容错镇定的充分条件;运用线性矩阵不等式(Linear Matrcx Inequality,LMI)方法,对Delta算子不确定系统进行状态反馈设计,给出了系统在区域极点约束下鲁棒容错控制器存在的充分条件,并通过求解LMI得到鲁棒容错控制器的设计.通过数值仿真验证了该方法的有效性.     

煤炭运输路径选择与运输效率的关系模型仿真

在煤炭运输路径优化选择问题的研究中,煤炭运输网络在雨雪、泥石流、地震等突发事件下会发生路线的重新选择,选择路线过程带有波动性与随机性,受主观人为因素影响较大.传统的煤炭运输供应链路径选择与运输效率的关系模型建立方法无法对瞬时变化的路线选择进行自适应调整,无法建立包含路线突变情况的约束条件,建立的模型准确性较低.提出基于关联模糊理论的煤炭运输供应链路径选择与运输效率的关系模型建立方法,塑造可量化的煤炭运输供应链网络演化模型,依据供应链网络鲁棒性新测度,根据小范围供应链效率和连同图大小运算网络中单个节点的运输效率,将量化后的鲁棒性与效率的关系划分成两个独立的特征子空间以及非特征子空间,准确描述两者关系.仿真结果表明,在不同的干扰环境下新建模型能够较好地表述煤炭供应链路径选择与效率的关系.     

PEAK-INSENSITIVE NON-PARAMETRIC SPECTRUM ESTIMATION

Abstract. We study the problem of non-parametric spectrum estimation of a stationary time series that might contain periodic components. In this case the periodogram ordinates have a significant amplitude at frequencies near the frequencies of the periodic components. These can be regarded as outliers in an asymptotically exponential sample. We develop a non-parametric estimator for the spectral density that is insensitive to these outliers in the frequency domain. This is done by robustifying the usual kernel estimator (smoothed periodogram) by means of M-estimation in the frequency domain. We propose to use data-tapered periodograms, which yield a drastic improvement of the procedure, typically for the contaminated situation. This is both shown theoretically and supported by means of simulation. We show consistency of the resulting estimator in the general case, and asymptotic normality in the special case of a Gaussian time series, whether contamination is present or not. Finally we illustrate the finite sample performance of the estimating procedure by some simulation results and by application to the Canadian lynx trappings data.     

观测误差协方差估计下的集合鲁棒滤波数据同化方法

在数据同化方法中,观测误差协方差矩阵是相关的,且与时间和状态有一定的依赖性。针对这种相关特性,将鲁棒滤波方法与观测误差协方差估计方法相结合,得到随状态时间变化的观测误差协方差,提出一种带有观测误差估计的鲁棒数据同化新方法,更新观测误差协方差,改善估计效果。从分析误差协方差,转移矩阵特征值放大等角度优化同化方法。利用非线性Lorenz-96混沌系统,对三种不同优化角度下带有观测误差估计的鲁棒滤波和原鲁棒滤波方法的鲁棒性和同化精度进行评估,并比较分析了两种方法在模型误差、观测数目和性能水平系数变化时的性能。结果表明：观测误差估计技术能够提高状态估计的精确性,带有观测误差估计的鲁棒滤波对系统参数变化具有较好的鲁棒性。