PID控制器的性能监控与评估

为了解决目前工业系统中普遍使用的PID控制器由于工况变化等原因引起的系统发生时变而导致所在回路PID控制器性能可能下降的问题,本文提出了一种专门针对PID控制器进行性能评估、优化及监控的方法,即:PID循环评估优化算法。该算法利用系统闭环输入输出数据,使用基于MVC(minimum variance control)的PID最小方差准则,来对PID控制器的性能进行评估,并且计算出在最小方差意义下最优PID控制器参数;评估过程结果与现实系统输出方差进行比较,做为PID参数在线优化的判断依据,当现实系统性能低于某一标准的时候对控制器进行优化处理。在整个算法中,通过输入输出数据的处理与判断,利用评估优化后的PID参数对系统进行控制,并再次回到最初的输入输出数据的处理和判断过程,实现在控制过程中的系统性能监控。本文的计算机仿真试验验证了该方法的有效性,由图可看出,系统发生渐变和突变后,当输出方差超过了程序限定的标准时,在1 300秒内系统能自动评估并施行优化而达到稳定。该循环评估优化算法现实了在对系统进行性能评估监控的同时,能按照一定条件作为标准对系统的PID参数进行优化,最终使得系统具有自我监控评估和自我优化的能力...     

热工系统实用控制性能评价研究

面对热工系统控制回路多,控制要求高的现状,对其进行有效的性能评价是必要的。基于此,一种荩于方差上下界的随机性能指标被提出,通过该指标可以判断控制系统的状态,并能给出其性能优化提升建议。然后针对指标实际应用中面临稳态数据选取、采样速率与控制速率不一致的两个问题,分别给出了基于最小熵的稳态数据选取办法和分段线性插值的数据重采样算法来解决。最后,通过仿真验证了性能指标和算法的有效性,并在某1000MW火力发电机组的主汽压控制系统中进行了应用。     

Economic performance assessment of advanced process control with LQG benchmarking

Economic performance assessment of advanced process control is conducted to investigate performance potentials that can be obtained by control system improvement. An optimization-based approach for economic performance assessment of the constrained process control is integrated with the LQG benchmark in this paper. By explicitly incorporating uncertainty into the performance assessment problem, economic performance evaluation can be formulated as a stochastic optimization problem, which helps to identify the opportunity to improve profitability of the process by taking appropriate risk levels. Using the LQG benchmark to estimate achievable variability reduction through control system improvement, the proposed method provides an estimate of both the performance that can be expected from the improved control system and the operating condition that delivers the improved performance. The results obtained can serve as a tool for control engineers to make decisions on control system tuning and/or upgrading. The proposed algorithm is illustrated via simulation examples as well as an industrial example.     

面向大数据存储的主动与被动相结合的性能评测方法体系结构与实现

随着数据量的日益增加,大数据存储在整个大数据应用框架体系中居于重要地位。对大数据存储系统进行性能评测可以指导大数据应用开发人员分析性能瓶颈,进行大数据系统的性能优化。在以往的工作中,通常使用基准测试的方式来对不同大数据框架进行性能评测,或者采用插桩并分析轨迹文件的方式对分布式文件系统进行性能分析。这2种方法采用的分析角度不同,并没有形成合理的评测体系来评价大数据分布式存储系统。本文提出主动与被动相结合的大数据存储系统性能评测方法体系结构及其具体实现。在主动性能评测方法方面,提供了6个领域,超过20个应用的基准测试程序,对大数据存储系统主动发起性能测试,分析大数据存储系统的基准性能指标;在被动性能评测方法方面,提供了对低效任务、低效算子、低效函数的分析及定位方法,通过分析运行在大数据存储系统之上的大数据应用,分析大数据应用程序低效的原因。通过实验表明,该大数据性能评测方法体系结构能够全面地对大数据存储系统进行性能评测。     

Performance assessment of thermal power plant load control system based on covariance index

This paper proposes a novel method of performance assessment for load control system of thermal power unit. Load control system is the most important multivariable control system. It is necessary to monitor and evaluate the performance of it. The performance evaluation index system based on covariance is defined, and the performance evaluation rules are given. In MATLAB, the double input and double output object model of the load control system is established, and the dynamic characteristics of the load control system are analyzed under the BF and TF mode. The simulation data, which is based on the parameters retuning, is used as the “benchmark data”, and the simulation data of different controllers are collected as “monitoring data”. For most of the time, the thermal power plant is under the coordinated control mode, and the principle and strategy of the two coordinated control are analyzed, and the engineering realization scheme is given. Operation data in different time periods of two different thermal power plants was acquisition and preprocessing respectively. The principle of selecting “benchmark data” is the minimum of pressure parameter. Two data segments were selected as “benchmark data”, performance assessment and analysis was carried on the data from other time periods. The results show that the validity and reliability of the method based on the evaluation index. In short, the data of the simulation and the load control system of power plant are used to demonstrate the effectiveness of the method.     

Control strategy PSO

Many variants of particle swarm optimization (PSO) both enhance the performance of the original method and greatly increase its complexity. Motivated by this fact, we investigate factors that influence the convergence speed and stability of basic PSO without increasing its complexity, from which we develop an evaluation index called “Control Strategy PSO” (CSPSO). The evaluation index is based on the oscillation properties of the transition process in a control system. It provides a method of selection parameters that promote system convergence to the optimal value and thus helps manage the optimization process. In addition, it can be applied to the characteristic analyses and parameter confirmation processes associated with other intelligent algorithms. We present a detailed theoretical and empirical analysis, in which we compare the performance of CSPSO with published results on a suite of well-known benchmark optimization functions including rotated and shifted functions. We used the convergence rates and iteration numbers as metrics to compare simulation data, and thereby demonstrate the effectiveness of our proposed evaluation index. We applied CSPSO to antenna array synthesis, and our experimental results show that it offers high performance in pattern synthesis.     

基于混合类电磁机制算法的混沌系统控制与同步

混沌系统控制与同步可通过优化方法设计控制律引导混沌系统轨道来实现．类电磁机制优化算法（EM）是模拟电磁场带电粒子间吸引一排斥行为机制的一种启发式搜索方法,目前还尚未在混沌系统控制与同步问题中得到应用．本文提出一种混合类电磁机制优化算法（HEM）用于求解该优化问题,该方法采用修改的类电磁机制算法（REM）与差分进化算法（DE）相融合平衡算法对解空间的全局探索和局部开发能力,基准函数测试表明混合算法改善了全局搜索能力及求解可靠性．在此基础上,采用HEM算法引导混沌系统的轨道,搜索施加于系统的小扰动使其轨迹在短时间内跟踪到目标区域;再将混沌系统的同步问题转化为在线轨道导引问题,采用HEM优化算法解决．通过典型离散Henon映射为例,数值仿真结果表明了该方法是解决混沌系统控制与同步的一种有效方法．     

A <Emphasis Type="Italic">Q</Emphasis>-learning-based swarm optimization algorithm for economic dispatch problem

In this paper, we treat optimization problems as a kind of reinforcement learning problems regarding an optimization procedure for searching an optimal solution as a reinforcement learning procedure for finding the best policy to maximize the expected rewards. This viewpoint motivated us to propose a Q-learning-based swarm optimization (QSO) algorithm. The proposed QSO algorithm is a population-based optimization algorithm which integrates the essential properties of Q-learning and particle swarm optimization. The optimization procedure of the QSO algorithm proceeds as each individual imitates the behavior of the global best one in the swarm. The best individual is chosen based on its accumulated performance instead of its momentary performance at each evaluation. Two data sets including a set of benchmark functions and a real-world problem—the economic dispatch (ED) problem for power systems—were used to test the performance of the proposed QSO algorithm. The simulation results on the benchmark functions show that the proposed QSO algorithm is comparable to or even outperforms several existing optimization algorithms. As for the ED problem, the proposed QSO algorithm has found solutions better than all previously found solutions.     

CloudBM:云数据管理系统测试基准

随着云计算技术和云数据管理技术的不断发展,越来越多的云数据管理系统纷纷面世,为人们提供了一种以经济实用的方式管理海量数据的方案。面对各种各样纷繁复杂的云数据管理系统,如何结合不同的应用场景对它们进行全面详细的性能评价是目前亟待解决的挑战性问题。以电信业务中用户访问记录存储系统为应用背景,结合云数据管理系统的应用特点,设计了一个具有广泛代表性的测试基准,并对目前主流的云数据管理系统进行了实际测试。通过对测试结果进行对比分析,为用户提供了一个可供参考的性能评价结果,同时验证了该测试基准的可靠性和广泛适用性。     

基于多模型混合最小方差控制的时变扰动控制系统性能评估

在实际工业过程中,控制系统经常会受到时变扰动的影响,致使针对单一扰动模型设计的最小方差控制准则不再适用于评估时变扰动控制系统的性能. 当多个扰动信号同时出现时,采用常规多模型切换方法会发生间歇切换进而产生较大的暂态误差,不能准确评估系统当前性能. 针对上述问题,本文提出了一种基于多模型混合最小方差控制准则的性能评估方法. 首先根据每个扰动模型分别制定最小方差控制器,组成多模型最小方差控制器,然后在每个时间点混合多模型最小方差控制器,并将在其作用下的输出方差作为最终的性能评估基准,该方法既 充分考虑到每个扰动的特性,又避免了常规多模型切换方法因间歇切换而产生的暂态误差对评估结果准确性带来的影响,实现了准确、可靠地评估时变扰动控制系统的性能. 通过仿真,验证了基于多模型混合最小方差控制准则的性能评估方法的有效性.     

A novel fused neural network controller for lateral control of autonomous vehicles

We present a novel fused feed-forward neural network controller inspired by the notion of task decomposition principle. The controller is structurally simple and can be applied to a class of control systems that their control requires manipulation of two input variables. The benchmark problem of inverted pendulum is such example that its control requires availability of the angle as well as the displacement. We demonstrate that the lateral control of autonomous vehicles belongs to this class of systems and successfully apply the proposed controller to this problem. The parameters of the controller are encoded into real value chromosomes for genetic algorithm (GA) optimization. The neural network controller contains three neurons and six connection weights implying a small search space implying faster optimization time due to few controller parameters. The controller is also tested on two benchmark control problems of inverted pendulum and the ball-and-beam system. In particular, we apply the controller to lateral control of a prototype semi-autonomous vehicle. Simulation results suggest a good performance for all the tested systems. To demonstrate the robustness of the controller, we conduct Monte-Carlo evaluations when the system is subjected to random parameter uncertainty. Finally experimental studies on the lateral control of a prototype autonomous vehicle with different speed of operation are included. The simulation and experimental studies suggest the feasibility of this controller for numerous applications.     

Particle swarm optimization with quantum infusion for system identification

System identification is a challenging and complex optimization problem due to nonlinearity of the systems and even more in a dynamic environment. Adaptive infinite impulse response (IIR) systems are preferably used in modeling real world systems because of their reduced number of coefficients and better performance over the finite impulse response filters. Particle swarm optimization (PSO) and its other variants has been a subject of research for the past few decades for solving complex optimization problems. In this paper, PSO with quantum infusion (PSO–QI) is used in identification of benchmark IIR systems and a real world problem in power systems. PSO–QI’s performance is compared with PSO and differential evolution PSO (DEPSO) algorithms. The results show that PSO–QI has better performance over these algorithms in identifying dynamical systems.     

A local evaluation of vectorized documents by means of polygon assignments and matching

This paper presents a benchmark for evaluating the raster to vector conversion systems. The benchmark is designed for evaluating the performance of graphics recognition systems on images that contain polygons (solid) within the images. Our contribution is two-fold, an object mapping algorithm to spatially locate errors within the drawing and then a cycle graph matching distance that indicates the accuracy of the polygonal approximation. The performance incorporates many aspects and factors based on uniform units while the method remains non-rigid (thresholdless). This benchmark gives a scientific comparison at polygon level of coherency and uses practical performance evaluation methods that can be applied to complete polygonization systems. A system dedicated to cadastral map vectorization was evaluated under this benchmark and its performance results are presented in this paper. By stress testing a given system, we demonstrate that our protocol can reveal strengths and weaknesses of a system. The behavior of our set of indices was analyzed when increasing image degradation. We hope that this benchmark will help assessing the state of the art in graphics recognition and current vectorization technologies.     

Influence of meta-heuristic optimization on the performance of adaptive interval type2-fuzzy traffic signal controllers

Intelligent traffic control systems optimized using meta-heuristic algorithms can greatly alleviate traffic congestions in urban areas. Meta-heuristics are broadly used as efficient approaches for complex optimization problems. Comparing the performance of optimization methods on different applications is a way to evaluate their effectiveness. The current literature lacks studies on how performance of traffic signal controllers is affected by utilized optimization algorithms. This paper evaluates the performance of three meta-heuristic optimization methods on an advanced interval type-2 adaptive neuro-fuzzy inference system (IT2ANFIS)-based controller for complex road networks. Simulated annealing (SA), genetic algorithm (GA), and the cuckoo search (CS) are applied for optimal tuning of IT2ANFIS controller. Optimizations methods adjust the parameters in a way to reduce the total travel time of vehicles in the road network. Paramics is used to design and simulate urban traffic network models and implement proposed timing controllers. Comprehensive simulation and performance evaluation are done for both single and multi-intersection traffic networks. Obtained results reveal significant superiority of IT2ANFIS trained using CS method over other controllers. The average performance of the CS-IT2ANFIS is about 31% better than the benchmark fixed-time controllers. This is 17% and only 3% for GA-IT2ANFIS and SA-IT2ANFIS controllers respectively.     

Multiple-model based predictive control of nonlinear hybrid systems based on global optimization using the Bernstein polynomial approach

In this paper, we propose a Bernstein polynomial based global optimization algorithm for the optimal feedback control of nonlinear hybrid systems using a multiple-model approach. Specifically, we solve at every sampling instant a polynomial mixed-integer nonlinear programming problem arising in the model predictive control strategy. The proposed algorithm uses the Bernstein polynomial form in a branch-and-bound framework, with new ingredients such as branching for integer decision variables and fathoming for each subproblem in the branch-and-bound tree. The performance of the proposed algorithm is tested and compared with existing algorithms on a benchmark three-spherical tank system. The test results show the superior performance of the proposed algorithm.     

基于比较策略的嵌入式系统性能基准测试研究

嵌入式系统受成本、功耗、芯片体积和开发周期等多种因素的制约,其性能往往难以满足应用需求,充分发挥嵌入式系统的潜能对嵌入式系统设计尤为重要。为此,需要对嵌入式系统进行性能基准测试,指导嵌入式系统设计的技术选型和决策,在满足应用需求的前提下,达到最佳性能/价格比。该文分析了嵌入式系统性能基准测试的基本原理和性能指标,介绍了几种常用的嵌入式系统性能评测基准及其适用范围,提出了一种运用对比策略进行嵌入式系统性能测试的测试方法和测试环境的构成,实现了一种嵌入式性能测试工具,并对相关的嵌入式系统及构件进行了性能测试。最后对嵌入式系统性能基准测试研究存在的主要问题进行了评价。     

无人机辅助MEC系统中面向用户公平性的三维部署和卸载优化

针对无人机辅助移动边缘计算系统存在的用户公平性不足问题, 本文提出了一种面向用户公平性的三维部署和卸载优化算法. 该算法综合考虑用户匹配、无人机三维部署、计算资源分配、卸载因子对系统总时延及用户公平性的影响, 建立了一个最小化系统总时延的多元优化问题, 并针对该问题提出了一种两阶段联合优化算法, 其中第1阶段使用带有平衡约束的聚类算法解决用户匹配和无人机的水平部署问题, 第2阶段使用凸优化算法迭代求解无人机高度部署, 资源分配和卸载因子优化问题. 实验结果表明, 与4种基准算法相比, 所提算法在系统总时延和用户公平性两方面具有更好的性能.     

Characterizing and optimizing TPC-C workloads on large-scale systems using SSD arrays在大规模系统上优化 TPC-C 评测程序

Transaction processing performance council benchmark C (TPC-C) is the de facto standard for evaluating the performance of high-end computers running on-line transaction processing applications. Differing from other standard benchmarks, the transaction processing performance council only defines specifications for the TPC-C benchmark, but does not provide any standard implementation for end-users. Due to the complexity of the TPC-C workload, it is a challenging task to obtain optimal performance for TPC-C evaluation on a large-scale high-end computer. In this paper, we designed and implemented a large-scale TPC-C evaluation system based on the latest TPC-C specification using solid-state drive (SSD) storage devices. By analyzing the characteristics of the TPC-C workload, we propose a series of system-level optimization methods to improve the TPC-C performance. First, we propose an approach based on SmallFile table space to organize the test data in a round-robin method on all of the disk array partitions; this can make full use of the underlying disk arrays. Second, we propose using a NOOP-based disk scheduling algorithm to reduce the utilization rate of processors and improve the average input/output service time. Third, to improve the system translation lookaside buffer hit rate and reduce the processor overhead, we take advantage of the huge page technique to manage a large amount of memory resources. Lastly, we propose a locality-aware interrupt mapping strategy based on the asymmetry characteristic of non-uniform memory access systems to improve the system performance. Using these optimization methods, we performed the TPC-C test on two large-scale high-end computers using SSD arrays. The experimental results show that our methods can effectively improve the TPC-C performance. For example, the performance of the TPC-C test on an Intel Westmere server reached 1.018 million transactions per minute.     

神经程序修复领域数据泄露问题的实证研究

修复软件缺陷是软件工程领域一个无法回避的重要问题,而程序自动修复技术则旨在自动、准确且高效地修复存在缺陷的程序,以缓解软件缺陷所带来的问题.近年来,随着深度学习的快速发展,程序自动修复领域兴起了一种使用深度神经网络去自动捕捉缺陷程序和其补丁之间关系的方法,被称为神经程序修复.从在基准测试上被正确修复的缺陷的数量上看,神经程序修复工具的修复性能已经显著超过了非学习的程序自动修复工具.然而,近期有研究发现神经程序修复系统性能的提升可能得益于测试数据在训练数据中存在,即数据泄露.受此启发,为进一步探究神经程序修复系统数据泄露的原因及影响,更公平地评估现有的系统,本文(1)对现有神经程序修复系统进行了系统的分类和总结,根据分类结果定义了神经程序修复系统的数据泄露,并为每个类别的系统设计了数据泄露的检测方法;(2)依照上一步骤中的数据泄露检测方法对现有模型展开了大规模检测,并探究了数据泄露对模型真实性能与评估性能间差异的影响以及对模型本身的影响;(3)分析现有神经程序修复系统数据集的收集和过滤策略,加以改进和补充,在现有流行的数据集上基于改进后的策略构建了一个纯净的大规模程序修复训练数据集,并验证了该数据集避免数据泄露的有效性.实验结果发现:本次调研的10个神经程序修复系统在基准测试集上均出现了数据泄露,其中神经程序修复系统RewardRepair的数据泄露问题较为严重,在基准测试集Defects4J(v1.2.0)上的数据泄露达24处,泄露比例高达53.33%.此外,数据泄露对神经程序修复系统的鲁棒性也造成了影响,调研的5个神经程序修复系统均因数据泄露产生了鲁棒性降低的问题.由此可见,数据泄露是一个十分常见的问题,且会使神经程序修复系统得到不公平的性能评估结果,以及影响系统本身的性质.研究人员在训练神经程序修复模型时,应尽可能避免出现数据泄露,且要考虑数据泄露问题对神经程序修复系统性能评估产生的影响,尽可能更公平地评估系统.     

Time domain constrained H∞‐synthesis

Structured H_∞‐synthesis subject to time domain performance specifications is introduced. Our method allows to control trajectories of the linearized system and the underlying nonlinear dynamics simultaneously. A non‐smooth bundle optimization method for this class of programs is proposed and discussed. Our approach is tested against two benchmark studies: control of a rotational actuator to attenuate vibration noise, and control of a continuous crystallizer. Our algorithm gives a local convergence certificate and is suited for systems with large state dimension. Copyright © 2010 John Wiley & Sons, Ltd.