Integrating structure and control design to achieve mixed H2/H performance

Present technology in structure design (smart structures, civil structures and aerospace structures) includes the use of feedback control. While retrofitting such active elements can be useful in existing structures, future designs will require something more than retrofitting technology. Future technology will certainly require a more systematic integration of the design of a structure and its active elements. This paper provides a step in that direction. We seek to integrate the design of the structure with its active elements to achieve mixed H2/H performance for the controlled structural system (closed loop system). More specificaly, the approach presented here solves a mixed passive control (structure design) and active control (feedback control law design) problems with performance characterized by system norms such that H performance bounds are guaranteed with les active energy. This approach al ows us to answer the question 'what is an optimal distribution of mass, stiffness, damping and control energy throughout a structure?' The main conclusion drew in this paper is that control design tools can be useful for structure design.     

基于MAS的分布式集成智能控制系统开发与应用

针对复杂工业过程，研究一种基于Multi—Agent system（MAs）的集成智能控制系统，系统由人机集成单元、控制单元和虚拟仿真单元构成，人机集成单元提供人类智慧与机器相结合有效机制，虚拟仿真单元为控制决策和故障诊断提供仿真信息，控制单元完成系统监视、诊断和控制任务，文中提出了实时Agent的组织和演化机制，采用遵循FIPA组织标准的开发工具JADE开发原型系统，描述了系统的实现，工程应用表明，提出的集成控制系统能有效地解决焦炉集气管压力这类复杂工业对象的过程控制问题．     

人工智能系统故障分析原理研究

为研究未来系统在人工智能控制下的系统故障预测、预防、控制和恢复能力,提出一种基于信息生态方法论的人工智能系统故障分析方法。将研究对象划分为人、功能、自然和智能系统;以智能系统为核心,研究故障信息、知识和智能安全生成原理;论述了基础故障意识、情感和理智的特点。研究表明,系统故障的人工智能分析必须采用信息生态方法论结合安全科学理论进行。分析原理是基于信息生态方法论,考虑基础故障意识、情感与理智,及即时故障语义信息进行的综合决策与反应,以确保系统在规定条件下完成预定功能。     

基于Multi-agent的分布式电梯故障诊断系统的研究

采用基于分布式人工智能的思想,将多Agent技术引入电梯故障诊断领域,分析了基于MAS的分布式智能故障诊断方法和过程;研究讨论了基于MAS的分布式智能故障诊断系统中Agent之间的调度协同求解方法和机制;给出了知识共享与重用的方法;建立了一种分布式多Agent诊断系统结构及其原型系统;通过在电梯故障诊断的应用仿真,提高了故障诊断的实时性、准确性和整体性能。     

基于神经网络和混合遗传算法的优化控制研究

本文主要提出一种对复杂系统进行智能优化控制的解决方案,首先采用神经网络对欲控制的复杂系统进行建模,然后采用实际数据构建针对性的神经网络模型,最后用混合遗传算法对神经网络模型进行制定参数寻优,从而获得对复杂系统的智能优化控制。在理论探讨的基础上,本文给出了此优化控制方法的相关MATLAB程序,只要针对具体问题稍加调整,相信此程序能较好地解决实际中复杂系统的优化控制问题。     

The Central Role of Heuristic Search in Cognitive Computation Systems

This paper focuses on the relation of heuristic search and level of intelligence in cognitive computation systems. The paper begins with a review of the fundamental properties of a cognitive computation system, which is defined generally as a control system that generates goal-directed actions in response to environmental inputs and constraints. An important property of cognitive computations is the need to process local cues in symbol structures to access and integrate distal knowledge to generate a response. To deal with uncertainties involved in this local-to-distal processing, the system needs to perform heuristic search to locate and integrate the right set of distal structures. The level of intelligence of the system depends critically on the efficiency of the heuristic search process. It is argued that, for a bounded rationality system, the level of intelligence does not depend on how much search it needs to do to accomplish a task. Rather, the level of intelligence depends on how much search it does not need to do to achieve the same level of performance. Examples were discussed to illustrate this idea. The first two examples show how machines that play games like tic-tac-toe and chess rely heavily on the efficiency of the heuristic search algorithm to achieve better performance, demonstrating the relation of heuristic search and intelligence in a bounded rationality system. The second example shows how humans adapt to different information ecologies to perform information search on the Internet and how their performance improves over time, demonstrating how heuristic search can be improved in an adaptive rationality system. The two examples demonstrate how better search control knowledge and representations of task environment can improve the efficiency of heuristic search, thereby improving the intelligence of the system.     

Visionary automation of sack handling and emptying

Describes an inexpensive yet “intelligent” prototype system using vision-guided robotics for depalletizing and emptying polyethylene sacks. It uses structured light, and ultrasonics to obtain depth information. A structure is presented that is designed and built to minimize costs and increase reliability of the whole system, while at the same time fully automating the depalletizing and emptying of polyethelene sacks. The system is built around a conventional PC that carries out the tasks of control, vision, gripping, and fault monitoring. Simple PID loops are used for the control of all the robot axes, while an inexpensive vision system is used for the determination of the orientation of the pallets. A pneumatic gripper is used for the lifting of the sacks. The most probable faults are monitored for and appropriate actions are taken in the event of fault occurrence. In this way, a form of intelligence is built into the system     

人工智能技术在矿山智能化建设中的应用初探

介绍了人工智能技术的相关概念、发展概述及其在煤炭行业发展中的应用,指出目前人工智能技术在矿山应用只是点状结合和浅度结合,没有实现人工智能技术和矿山某个生产或管理系统层面的深度融合。概述了智能矿山的发展历程,指出智能矿山是人工智能技术、大数据技术、物联网技术和矿山实体的深度融合体,利用智能通信、智能控制和智能计算技术实现数字化矿山的计算、处理,构建数字孪生矿山,通过数字孪生矿山和物理矿山的智能交互演化,达到对煤矿安全、高效、绿色的生产控制。构建了将人工智能技术和矿山深度融合的包括设备层、智能层、应用层的智能矿山三层构架:应用层处于智能矿山的最高层,其中的数字孪生矿山子层相当于“数字大脑”,实现矿山最高层次的智能控制;智能层中的智能体要求子系统不仅仅是应用人工智能技术处理子系统所产生的数据,而是从架构上就要将智能计算、智能通信、智能控制融为一体。展望了智能矿山建设的发展趋势:智能化矿山需要加强人工智能技术和矿山融合度的深入研究,将现有的基于人工智能的故障检测、诊断及超前干预技术应用到机器人系统中,智能计算、智能通信、智能控制融合的巡检机器人将是最早能推广的井下智能体之一;智能化矿山需要进一步加强复杂巨系统建模技术的研究,只有建立了矿山的复杂巨系统模型,才能实现采矿活动和环境的协同互动,实现采煤活动的精准控制,复杂巨系统模型的缺乏将是未来智能矿山建设亟需解决的问题。     

基于过程统计的轧机厚控系统故障诊断

针对采用设备故障诊断方法对轧机厚控系统设备部件进行故障诊断时,只能对厚控系统的局部环节进行诊断的不足,评价了轧机厚控系统的整体性能。采用过程统计的方法对带钢厚度偏差的分布规律进行实时分析,建立带钢出口厚度偏差的过程统计控制图,并结合轧机设备运行过程中一些必要的设备状态信息,从机理上诊断出几类典型的故障现象,从而完善了轧机厚控系统的故障诊断。     

基于专家—神经网的递阶智能控制系统

本文介绍了专家系统与人工神经网络技术相结合的一些方法，提出了联合使用专家系统与人工神经网络的递阶智能控制系统结构，其组成为：监督控制专家系统，数据处理与故障诊断神经网络，常规分布式控制系统。其中的监督控制专家系统又由人工神经网络模拟实现。由于本系统吸取了专家系统技术及人工神经网络技术两者的长处，克服了各自的缺点，使得系统的性能大为提高。     

Multiobjective optimization–based fault‐tolerant flight control system design

The loss of measurements used for controller scheduling or envelope protection in modern flight control systems due to sensor failures leads to a challenging fault‐tolerant control law design problem. In this article, an approach to design such a robust fault‐tolerant control system, including full envelope protections using multiobjective optimization techniques, is proposed. The generic controller design and controller verification problems are derived and solved using novel multiobjective hybrid genetic optimization algorithms. These algorithms combine the multiobjective genetic search strategy with local, single‐objective optimization to improve convergence speed. The proposed strategies are applied to the design of a fault‐tolerant flight control system for a modern civil aircraft. The results of an industrial controller verification and validation campaign using an industrial benchmark simulator are reported.     

FDT技术及其应用

FDT（Field Device Tool）是现场设备集成的新技术,人们利用它可以方便地把现场设备集成到控制系统中并实现设备的互操作．在一种集成的平台下对智能设备进行调试、组态、诊断和统一的管理。详细地介绍了FDT的原理和整体的框架结构,并分析FDT技术的应用及其发展的前景。     

Fault isolation in analog circuits using a fuzzy inference system

This paper introduces a fuzzy inference system (FIS) for single analog fault diagnosis. The ability of fuzzy logic to encode structured knowledge in a numerical framework is exploited in isolating faults in analog circuits. A training set that simulates the behaviour of the circuit due to a set of anticipated single faults as well as the fault-free situation is first constructed. For each anticipated fault, this set relates the circuit measurements to the corresponding deviation in the faulty circuit element from its nominal. These measurements and the deviations in circuit elements are both fuzzified into appropriate linguistic fuzzy values. A fuzzy rule base for each fault that characterizes the circuit response by linking symptoms to causes is built. The outputs of the fuzzy rule bases are then defuzzified to recover crisp values for the deviations in circuit elements. A fault diagnosis procedure that utilizes the proposed FIS is also presented along with a brief analysis and comparison with a number of existing artificial intelligence-based techniques. A test example that demonstrates the potential of this procedure in fault isolation is illustrated.     

安全科学中的故障信息转换定律

为在安全科学领域实现从故障信息到安全决策过程,本文提出了安全科学中的故障信息转化定律。安全科学核心之一是系统可靠性。随着信息数据和智能科学的发展,系统可靠性理论也应跟随并发展。信息生态方法论是不同于传统机械还原论的方法论,可全方位研究故障信息。因素空间理论是基于因素的智能科学数学基础。空间故障树理论则是研究可靠性与因素关系的系统科学方法。因此三者在因素、系统变化及其特征研究方面具有天然的集成性。分别提供了研究系统可靠性的方法论、智能数学基础和具体实施平台。信息生态方法论和因素空间理论可指导并融入空间故障树理论的发展。从而为安全科学基础理论提供符合信息和智能科学的系统可靠性分析理念和方法,为安全科学的信息化及智能化作出尝试。     

基于多传感器信息融合的控制系统研究

本文研究了多传感器组合系统的智能化数据融合方法。从数据处理算法、故障检测和系统可靠性等方面探讨 了基于多传感器的控制系统,并提出了一种实用的数据处理算法和故障检测算法。     

Intelligent distributed and supervised flow control methodology for production systems

This paper deals with the development of an intelligent distributed and supervised control approach for high-volume production systems, in which the flow of parts can be approximated by a continuous (fluid) model. The proposed approach is based on the decomposition of the production system into elementary modules in order to reduce the control design computational complexity. In this context, a two levels control structure is proposed. At the local level, a surplus-based principle is adopted to regulate the production flow for each module according to the distributed structure. The proposed control methodology decides how to adjust the production rate in order to avoid system overloading and eliminate machine starvation or blocking. In this context, the local control law is synthesized by using the Takagi–Sugeno fuzzy systems. At the high level, a supervisory controller is designed to improve the overall system performances. A supervisor provides an additive component for each local controller when the overall system performances deviate from their acceptable domains (degraded mode). This is done by combining both local and global information into a unified formalism by using aggregation operators and according to fuzzy interval representation of the desired objectives. Finally, the feasibility of the proposed methodology is validated with simulation examples.     

基于网络的炼胶生产线智能控制系统研发

本文针对现有炼胶生产线控制系统存在的问题和企业信息化发展的需要,介绍了利用自行开发的智能控制系统对传统炼胶生产线进行基于网络的系统改造,综合考虑了改造成本、系统实时性、可靠性、可扩展性以及生产质量的在线预测和故障的在线诊断等几个主要因素,提高了系统的智能化程度和开放性。     

基于蝙蝠算法的系统级故障诊断研究

首次将蝙蝠算法用于解决系统级故障诊断问题,从而提出了一种高效的诊断算法——蝙蝠故障诊断算法。在初始化阶段,种群被分成大、小两类,并采用不同的处理方式;根据系统级故障模型的特点,设计出了具有方程约束的适应度函数;为了平衡全局搜索与局部搜索,在速度更新公式中增加一个变系数;为实现寻址的离散化,对蝙蝠速度进行了二进制映射。仿真实验结果表明,蝙蝠故障诊断算法在迭代次数、诊断正确率和最优解的适应度等方面明显优于现有的具有代表性群智能诊断算法——FAFD算法。     

火电供煤智能输配系统的研究与应用

针对火电厂现场煤粉输配需求,设计一种煤粉输配智能监控系统。系统按功能分为iFIX组态子系统、控制优化子系统、视频监控子系统和后台数据库管理子系统。分析配煤自动化的可编程逻辑控制、iFIX组态和优化控制过程,建立基于模糊决策的故障诊断和遗传算法的配煤和调度优化,采用VC++实现控制优化子系统功能以及各子系统间的通信。应用结果表明,该系统可实现煤粉输送控制自动化和优化混配智能化。     

Fault tolerant control for a class of nonlinear system with actuator faults

In this work, a fault tolerant control scheme is proposed for a class of nonlinear system with actuator faults. In this fault tolerant control strategy, an estimator is designed to estimate both the system states and the fault signal simultaneously. Based on these estimations, the control law is constructed to achieve the fault tolerant control for the nonlinear system considered. It is shown that the estimation error and the system state can be guaranteed to be bounded. The obtained theoretic results have been verified through the simulation examples on the three‐tank system.