Optimal back-off point determination and controller weight selection for multivariate systems under finite-horizon control

Plant economic performance is most often related to the operating point, specifically the mean values of the process variables; meanwhile, most existing performance assessment techniques involve examining the variances or covariances of the controlled variables. A combined approach is to determine the appropriate trade-off between variances of different process variables in order to operate the plant at the point that provides maximum economic benefit while satisfying the operating constraints. This problem is referred to as the minimum backed-off operating point selection, and previous works have formulated it as a non-convex constrained optimization problem. In the current work, a new technique is introduced that can provide the optimal plant operating point. Additionally, this method provides the weights for a finite horizon controller that results in the optimal trade-off in process variable variances that will allow satisfaction of the operating constraints at the optimal operating point. In this method, the plant and disturbance models for the given process are used to generate data representing possible trade-offs between process variable standard deviations. Employing a piecewise linear regression to describe the sample points of this standard deviations data allows for the operating point selection problem to be solved as a small number of linear programs. The advantages of this approach are demonstrated through the use of mathematical and simulation case studies.     

A novel model for arc territory design: promoting Eulerian districts

The problem of district design for the implementation of arc routing activities is addressed. The aim is to partition a road network into a given number of sectors to facilitate the organization of the operations to be implemented within the region. This problem arises in numerous applications such as postal delivery, meter readings, winter gritting, road maintenance, and municipal solid waste collection. An integer linear programming model is proposed where a novel set of node parity constraints to favor Eulerian districts is introduced. Series of instances were solved to assess the impact of these parity constraints on the objective function and deadhead distance. Networks with up to 401 nodes and 764 edges were successfully solved. The model is useful at a tactical level as it can be used to promote workload balance, compactness, deadhead distance reduction and parity in districts.     

MIC异构直线加速器束流动力学模拟并行软件开发

现代高能物理研究需要使用高能量的粒子加速器,加速器束流动力学模拟软件具有重要的实用意义. 介绍了一个3维基于MIC的异构直线加速器并行束流动力学模拟软件NEWBEAM-MIC的开发进展. 目的是使用最新的超级异构计算机提高束流动力学模拟软件的性能,更好地完成加速器的设计和优化工作. 这个软件模拟了DTL和SOLENOID加速器装置中粒子的运动过程. NEWBEAM-MIC是在NEWBEAM-CPU软件基础上,将粒子推进部分分配到MIC卡上运行,从而利用MIC多线程的优势使计算加速的. 通过实际测试,这个软件在天河二号上使用100 CPUs和100 MICs可以模拟10⁹个粒子,其中DTL场力计算、SOLENOID场力计算和粒子推进三个部分均可以比仅使用100 CPUs的NEWBEAM软件有100倍以上的加速效果. 再考虑MIC卡上的多线程,对同样规模的粒子,使用100 CPUs 和 100 MICs,当MIC线程数开到最大（224）时,NEWBEAM-MIC可以比单线程串行计算方式加速10000倍以上. 这表明本文开发的基于MIC的异构软件可以很好地加速原有的CPU软件,发挥现有MIC异构超级计算机的潜在性能.     

An integer program and a hybrid genetic algorithm for the university timetabling problem

The university timetabling problem (UTP) has been studied by numerous research groups for decades. In addition to addressing hard and soft constraints, we extend the UTP by considering consecutiveness and periodicity constraints of multi-session lectures, which are common in many eastern Asian universities. Because schedulers can decide the consecutiveness and periodicity constraints for the multi-session lectures within a limited ratio, we consider these novel decision variables in our model. We develop a mixed integer linear program for the UTP. For the analysis, we convert the UTP into the three-dimensional container packing problem (3DCPP) and create a hybrid genetic algorithm (HGA), which has been shown to be efficient in solving the 3DCPP. We also develop a tabu search algorithm based on the existing UTP literature and compare the findings with that of our HGA. The results show that our HGA obtains a better solution than the tabu search algorithm in a reasonable amount of time.     

The distributionally robust complementarity problem

We investigate the linear complementarity problem with uncertain parameters (ULCP) which affect the linear mapping affinely or quadratically. Assuming that the distribution of the uncertain parameters belongs to some ambiguity set with prescribed partial information, we formulate the ULCP as a distributionally robust optimization reformulation named as the distributionally robust complementarity problem (DRCP), which minimizes the worst case of an expected complementarity measure with a joint chance constraint that the probability of the linear mapping being nonnegative is not less than a given level. Applying the cone dual theory and S-procedure, we conservatively approximate the DRCP as a nonlinear semidefinite programming (NSDP) with bilinear matrix inequalities, which can be solved by the NSDP solver PENLAB. The preliminary numerical test on a constrained stochastic linear quadratic control problem shows that the DRCP as well as the corresponding solution method is promising.     

Observer‐based fault estimators using iterative learning scheme for nonlinear time‐delay systems with intermittent faults

This paper deals with the intermittent fault estimation problem for a class of nonlinear time‐delay systems with measurement noise. The time delays are assumed to occur in state vector, nonlinear term as well as output vector, thus reflecting the time delays influence in reality more closely. The aim of the problem is to estimate the intermittent fault by using iterative learning scheme, with the property of index, hence attenuating the influence from measurement noise. Different from existing fault estimating schemes, the state error information and fault estimating information in the previous iteration are used in the current iteration to improve the estimating results. The stability and convergence of iterative learning observer and uniform boundedness of dynamic error system are achieved by using Lyapunov function and optimal function design. Simultaneously, an improved sufficient condition for the existence of such an estimator is established in terms of the linear matrix inequality by the Schur complements and Young relations. Furthermore, the results are both suited for the systems with time‐varying delay and the systems with constant delay. Finally, two numerical examples are proposed to illustrate the effectiveness of the proposed method, and a comparability example is presented to demonstrate its superiority. Copyright © 2017 John Wiley & Sons, Ltd.     

Robust Output Feedback Controller Design for Time‐Delayed Teleoperation: Experimental Results

In this paper, a robust output feedback control strategy is proposed for a nonlinear teleoperation system which can deal with stability as well as transparency despite the variable time‐delay and uncertain dynamics. The proposed approach is composed of two steps. First, local Lyapunov based adaptive controllers are applied to both master and slave sides in order to suppress the nonlinearities in the system dynamics. Afterwards, a new observer‐based controller scheme is proposed to achieve stability and performance (transparency) of the teleoperation system. Using the Lyapunov techniques, stability and performance objectives are cast as some linear matrix inequality (LMI) feasibility conditions. To evaluate the performance of the proposed controller, a set of simulations and experiments are performed. Through simulation results, it is demonstrated that the proposed approach significantly outperforms the existing methodologies reported in the literature.     

Robust Stochastic Stability and Control for Uncertain Singular Markovian Jump Systems with Multiplicative Noise

This paper focuses on the problems of robust stability and stabilization and robust control for uncertain singular Markovian jump systems with (x,v)‐dependent noise. The parameter uncertainties appearing in state, input, disturbance as well as diffusion terms are assumed to be time‐varying but norm‐bounded. Based on the approach of generalized quadratic stability, the memoryless state feedback controller is designed for the robust stabilization problem, which ensures that the resulting closed‐loop system has an impulse‐free solution and is asymptotically stable in the mean square. Furthermore, the results of robust control problem are derived. The desired state feedback controller is presented, which not only meets the requirement of robust stabilization but also satisfies a prescribed performance level. The obtained results are formulated in terms of strict LMIs. What we have obtained can be viewed as corresponding extensions of existing results on uncertain singular systems. A numerical example is finally given to demonstrate the application of the proposed method.     

封闭型小区交通开放对城市交通的影响

随着交通需求的增加,城市交通问题日益严重,能否通过开放现有封闭型小区的道路以缓解城市交通拥堵是一个值得研究的问题.本文针对这一问题,建立了基于最短路的交通分流线性规划网络模型,提出了交通量改善因子、路段饱和度均值以及路段饱和度标准差等三个评价指标,以评价和对比封闭性小区开放前后的交通量与道路占用程度,从而反映了封闭性小区的开放对城市交通的具体影响.本文对两种不同的交通网络及交通流量数据进行了仿真与模型求解,结果表明：尽管小区开放后路网密度提高,有可能使得总交通负荷降低,但并非任何小区开放均能缓解城市交通拥堵.小区面积、位置、外部及内部道路状况等诸多因素都能影响路网的交通状态,所以,是否开放封闭型小区,需要用针对具体的小区与周边道路状况进行评估后再进行决策.     

基于改进的多流形算法的人脸图像集识别

一个图像集由大量变化不一的图像组成,而且这些图像都表示同一个人.现实中的图像集数据是非线性的,造成这些现象的因素有人脸的角度不同、光线的明暗等,因此图像集中的每幅图像都是变化的,如果近似的将一个图像集建模为线性子空间,而忽略了集合中数据结构的变化,很显然是不合理的,这也必然会影响到最后的识别率.受流形理论知识的启发,可以将图像集建模为一个流形,这与传统的将图像集建模为子空间的方法有着本质区别.本文在基于流形的人脸图像集识别方法的基础上进行改进,提出新的计算样子空间距离方法,最后采用所有最短子空间距离的平均值作为流形之间的距离,称为改进的多流形方法(Improved multi-manifold method,IMM).IMM方法在CMU PIE数据库上进行实验,结果表明该方法相比其他方法具有更高识别率.