基于规则的模糊离散事件系统建模与控制研究

用模糊离散事件系统（FDES）从离散事件的角度描述一类包含“具有确定性的不确定／模糊”问题以及与人的主观观察／判断密切相关的复杂系统。提出了基于规则的FDES描述方法及自寻优监督控制策略,为FDES的实际应用提供了一种方法,提高了FDES在具体应用中的可操作性,并介绍了其在纺织染整控制过程中的具体应用．     

离散事件系统部分可诊断性分析

针对离散事件系统部分可诊断性问题,提出一种量化评价与分析方法。该方法以树状结构的故障模型为基础,引入可诊断度与可诊断深度指标,能够从可诊断故障覆盖程度与精确程度两个方面对系统可诊断性进行评价,其优点是评价结果量化表示,能为部分可诊断系统的进一步评价、分析与对比提供参考。此外,还讨论了故障模型对系统可诊断度与可诊断深度两个评价指标的影响,并给出了故障模型构造的一般原则。实例分析与讨论结果表明,所提出的可诊断度与可诊断深度指标能够准确反映系统在特定故障模型下的部分可诊断状态。所提出的部分可诊断性评价方法能为基于离散事件模型的复杂系统设计与评价提供依据,并能够进一步为智能、自适应和自愈系统的设计提供参考。     

基于不确定观测下离散事件系统可诊断性的研究

在实际应用系统中,由于传感器故障、传感器限制和网络中的数据包丢失等原因,事件的可观测值变得不确定,使得观测系统行为变得尤为复杂。针对离散事件系统中,同个事件串可能有多个观测值以及不同状态下同个事件观测值也可能不同的问题,提出一种不确定观测下故障诊断验证的方法。首先对不确定观测的离散事件系统的可诊断性进行形式化,然后构建出用于上述故障诊断验证的验证器;基于验证器提出了系统基于不确定观测下可诊断的充要条件及验证算法;最后,实例说明不确定观测下故障诊断验证算法的应用。与现有研究相比,提出的方法对故障事件的观测值没有约束,可以为0个或多个观测值,使此方法应用的场景更为广泛。     

模糊离散事件系统的可测性

针对模糊离散事件系统的可测性问题，提出一种基于不可区分串的可测性判定方法。首先引入不可区分串概念以刻画对任一极小可观测事件具有相同的可观察行为；然后构造一个基于极小可观测事件的观察器，建立不可区分串与观察器中非单元循环的一一对应关系，并由此得到模糊可测性的充要条件。同时，也给出一个验证模糊离散事件系统可测性算法。通过实例分析，说明模糊离散事件系统可测性的应用。     

模糊离散事件系统基于验证器的模式故障诊断

针对模糊系统在运行过程中可能出现由多个事件触发的故障,研究模糊离散事件系统模式故障的诊断问题,提出一种基于验证器的模式故障诊断方法.先对模糊离散事件系统中最常见的模式故障,引入S类型模式故障和T类型模式故障两个概念,再分别对模糊离散事件系统的S类型和T类型模式故障的可诊断性进行形式化.为验证模糊系统模式故障的可诊断性,构造一个验证器自动机,并得到一个关于模糊离散事件系统模式故障可诊断性的充分必要条件,实现对模糊系统模式故障的诊断.     

Diagnosability of delay-deadline failures in fair real time discrete event models

In this article a method for failure diagnosis of real time discrete event systems (RTDES) with ‘fairness of traces’ has been developed. Discrete event system (DES) modelling framework with provision for associating timing information with the transitions are required for handling real time systems. RTDES models and timed DES (TDES) models are examples of such modelling frameworks. Failure diagnosis in untimed DES models enables only the study of diagnosability of failures resulting in a change in the logical behaviour of the failed system. In addition to logical failures, failure diagnosis in RTDES and TDES models also enables diagnosability of failures that change the timing behaviour of the system but maintain the logical behaviour. Many systems exhibit fairness of traces with respect to transitions in the sense that any trace that visits a state infinitely often has infinitely many occurrences of all the transitions that emanate from that state. The abstraction employed in obtaining their (timed) DES models often obliterates this property. The RTDES and TDES diagnosability conditions, proposed in the literature and which do not consider fairness, are shown to be inadequate in this article. A new diagnosability condition is achieved by taking into account this fairness property in the RTDES models and shown to be necessary and sufficient for such systems. An analysis of time complexity for analysing the diagnosability of systems with fairness of traces is presented.     

Polynomial-time verification of diagnosability of fuzzy discrete event systems

A fuzzy approach to perform diagnosis of fuzzy discrete event systems(FDESs)is proposed by constructing diagnosers,which may more effectively cope with the problems of vagueness and fuzziness arising from failure diagnosis of fuzzy systems.However,the complexity of constructing this kind of diagnosers is exponential in the state space and the number of fuzzy events of the system.In this paper,we present an algorithm for verifying the diagnosability of FDESs based on the construction of a nondeterministic automaton called F-verifier instead of diagnosers.Both the construction of F-verifiers and the verification of diagnosability of FDESs can be realized with a polynomial-time complexity.     

Distributed fuzzy discrete event system for robotic sensory information processing

Abstract: This paper presents a novel intelligent sensory information processing technique using a fuzzy discrete event system (FDES) for robotic control. The proposed method combines the predictive control approach of a discrete event system with the approximate reasoning aspect of fuzzy logic. It develops a supervisory control strategy for behavior-based robotic control using distributed FDES. The application of distributed FDES eliminates the formation of complex fuzzy predicates and a large fuzzy rule-base. The FDES-based approach also provides means for analyzing behavior-based decision-making using the observability and controllability of an FDES. The observability of an FDES describes uncertainties in sensory data, and the controllability of an FDES exploits uncertain state transitions in a dynamic environment. Comprehensive experiments on behavior-based mobile robot navigation are presented to authenticate the performance of the proposed methodology.     

From classic observability to a simple fuzzy observability for fuzzy discrete-event systems

In order to determine uncertainties from restricted available information, fuzzy discrete-event systems (FDESs), or fuzzy discrete-event dynamic systems (FDEDSs), were recently proposed. These frameworks include fuzzy states and events occurring simultaneously with different membership degrees. Fuzzy states and events have been used to describe uncertainties that occur often in practical problems, such as treatment planning for HIV/AIDS patients, sensory information processing for robotic control, and fault diagnosis problems. In order to measure information associated with FDESs or FDEDSs, the classical discrete event system (DES) observability has been turned into fuzzy observability for FDESs or FDEDSs. The newly proposed method allows ease of defining degrees of observability so that uncertainties in FDESs or FDEDSs can be dealt with effectively. This gives an opportunity to design better decision-making systems. To calculate the observability degree, a simple fuzzy observability checking method is introduced, and two examples are elaborated upon to illustrate the presented method. Finally, the newly proposed method is tested on a heating, ventilating, and air-conditioning (HVAC) system.     

具有多项式时间复杂性的离散事件系统安全诊断

离散事件系统的故障诊断能将已发生的不可观故障事件及时诊断出来,但往往容易忽略故障诊断期间系统的安全性.为解决这一问题,提出了一种具有多项式时间复杂性的安全故障诊断方法.先对离散事件系统的安全可诊断性进行了形式化,再通过构造一个非法语言识别器对系统被禁止操作序列进行识别,并在此基础上构建了一个对系统实施安全诊断的安全验证器,得到了一个关于离散事件系统安全可诊断性的充分必要条件,实现了对系统的安全故障诊断.同时,通过对安全验证器的构建与安全可诊断性的判定的复杂性分析,得到了该安全故障诊断方法可在多项式时间内实现等结论.     

模糊离散事件系统监督控制理论

为了将经典离散事件系统 (Discrete event systems, DES) 中的监控理论扩展到模糊离散事件系统 (Fuzzy discrete event systems, FDES) 中, 总结了目前 FDES 建模和监控理论方面的成果, 进一步讨论了 FDES 的能控性, 给出了非阻塞监控的条件. 对 FDES 的能控子语言与优化监控进行了分析, 得到了能控子语言的性质. 以模糊自动机作为监控的一种形式, 给出了监督控制具体的实现方法, 这对于进一步开展 FDES 的研究工作是有益的.     

不完备离散事件系统的可诊断性

在离散事件系统的建模过程中,由于系统行为的复杂,存在物理系统向逻辑系统映射的不完全性,因此产生了不完备模型的概念.提出在模型不完备的前提下,判断模型可诊断性的方法.提出可诊断性的在线判定方法,同时将不完备的行为加入模型,使模型完备.用经典的双树方法判断离线可诊断性,根据观测序列的时序及语言的前缀判断并处理不完备行为.提出判定不完备行为的方法,向模型中添加不完备行为,并根据不完备行为增量地在双树中判定在线可诊断性.通过在线的可诊断性判定,当前系统能够得到在有限观测内唯一判定故障发生与否的结论.该方法适用于具有离散性质的系统.     

Resilient Fault Diagnosis Under Imperfect Observations–A Need for Industry 4.0 Era

In smart industrial systems, in many cases, a fault can be captured as an event to represent the distinct nature of subsequent changes. Event-based fault diagnosis techniques are capable model-based methods for diagnosing faults from a sequence of observable events executed by the system under diagnosis. Most event-based diagnosis techniques rely on perfect observations of observable events. However, in practice, it is common to miss an observable event due to a problem in sensor-readings or communication/transmission channels. This paper develops a fault diagnosis tool, referred to as diagnoser, which can robustly detect, locate, and isolate occurred faults. The developed diagnoser is resilient against missed observations. A missed observation is detected from its successive sequence of events. Upon detecting a missed observation, the developed diagnoser automatically resets and then, asynchronously resumes the diagnosis process. This is achieved solely based on post-reset/activation observations and without interrupting the performance of the system under diagnosis. New concepts of asynchronous detectability and asynchronous diagnosability are introduced. It is shown that if asynchronous detectability and asynchronous diagnosability hold, the proposed diagnoser is capable of diagnosing occurred faults under imperfect observations. The proposed technique is applied to diagnose faults in a manufacturing process. Illustrative examples are provided to explain the details of the proposed algorithm. The result paves the way towards fostering resilient cyber-physical systems in Industry 4.0 context.      

基于动态观测的随机离散事件系统故障诊断

近年来,离散事件系统故障诊断研究引起国内外学者广泛关注.鉴于此,研究动态观测下随机离散事件系统的故障诊断.首先引入一种动态观测,使事件的可观测性随着系统的运行而动态变化;然后分别对基于动态观测的随机离散事件系统的单故障可诊断性和模式故障可诊断性进行形式化;最后通过构造相应的诊断器,分别得到关于单故障可诊断性和模式故障可...     

Decision making in fuzzy discrete event systems

The primary goal of the study presented in this paper is to develop a novel and comprehensive approach to decision making using fuzzy discrete event systems (FDES) and to apply such an approach to real-world problems. At the theoretical front, we develop a new control architecture of FDES as a way of decision making, which includes a FDES decision model, a fuzzy objective generator for generating optimal control objectives, and a control scheme using both disablement and enforcement. We develop an online approach to dealing with the optimal control problem efficiently. As an application, we apply the approach to HIV/AIDS treatment planning, a technical challenge since AIDS is one of the most complex diseases to treat. We build a FDES decision model for HIV/AIDS treatment based on expert’s knowledge, treatment guidelines, clinic trials, patient database statistics, and other available information. Our preliminary retrospective evaluation shows that the approach is capable of generating optimal control objectives for real patients in our AIDS clinic database and is able to apply our online approach to deciding an optimal treatment regimen for each patient. In the process, we have developed methods to resolve the following two new theoretical issues that have not been addressed in the literature: (1) the optimal control problem has state dependent performance index and hence it is not monotonic, (2) the state space of a FDES is infinite.     

Diagnosability of a class of discrete event systems based on observations

The diagnosability of discrete event systems has been a topic of interest to many researchers. The diagnosability conditions for various systems have evolved based on a regularity condition that is imposed on faulty traces with respect to their observable continuations. Improving upon this weak but necessary condition, a new model of diagnosability that is based on sensor outputs, which are called observations, upon a command input is proposed in this paper. Necessary and sufficient conditions are derived for the proposed diagnosability model. The search performance of the proposed diagnosability condition is of linear complexity in terms of the power set of the system events and observations, compared to the exponential complexity of the search with the existing diagnosability regularity condition. Moreover, a system that is not diagnosable according to the existing diagnosability condition may be diagnosable in the proposed diagnosability model, which includes observations.     

Diagnosability of fair discrete event systems

Failure diagnosability has been widely studied using discrete event system (DES) models. It is, however, shown in this work by means of a counterexample that the diagnosability condition, which has been shown to be necessary and sufficient in the DES context, fails to hold for many real‐world hybrid systems. This is because the abstraction employed in formulating the DES models obliterates the continuous dynamics. In the present work, a new failure diagnosability mechanism has been developed for discrete time hybrid system (DTHS) models to alleviate this problem. A new diagnosability condition is proposed and its necessity and sufficiency with respect to the diagnosability definition are established formally. Finally, the method of A‐diagnosability, which can also be used to circumvent this problem and which needs additional probabilistic information for diagnosability analysis, has been shown to have a higher computational complexity than the DTHS model based method proposed in this paper. Further, it is also highlighted that the DTHS model based diagnosability analysis technique is capable of diagnosing faults that degrade the temporal performance of the system, which cannot be handled by the A‐diagnosability analysis mechanism. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Diagnosability of discrete event systems and its applications

As man-made systems become more and more complex, diagnostics of component failures is no longer an easy task that can be performed based on experience and intuition. Therefore, it is important to develop a systematic approach to diagnostic problems. Diagnostics can be done either on-line or off-line. By on-line diagnostics, we mean diagnostics performed while the system to be diagnosed is in normal operation. On the other hand, in off-line diagnostics, the system is not in normal operation. We will study both on-line and off-line diagnostics in this paper and identify main features and differences of these two types of diagnostics. We will also introduce the concept of diagnosability and study its properties, all in the framework of discrete event systems. This study is motivated by diagnostic problems in the automotive industry and we will emphasize its applications.     

Theory of Extended Fuzzy Discrete-Event Systems for Handling Ranges of Knowledge Uncertainties and Subjectivity

In 2001, we originated a theory of fuzzy discrete-event systems (FDESs) that generalized the conventional/crisp discrete-event systems (DESs). Vagueness and imprecision concerning states and event transitions of DESs were represented by membership grades and computed via fuzzy logic. Our application of the FDES theory to computerized human immunodeficiency virus/acquired immune deficiency syndrome treatment regimen selection, although preliminarily successful, suggests that a more comprehensive FDES theory is needed to address two general issues critically important not only to biomedical applications, but also to real-world problems in other industries. First, domain experts should have means other than point estimates and type-1 fuzzy sets mandated in the current framework to describe uncertainties, subjectivity, and imprecision in their (complex) knowledge and experience. Second, when a group of experts with distinct opinions is involved, they should not be forced to reach consensus for the sake of system development. This is because collective consensus may not be achievable, which is often the case in medicine, where individual expertspsila opinions should be equally respected since the underlying ground truth is unknown most of the time. The theory of extended FDES presented in this paper addresses both the problems and contains the FDES theory as a special case. Experts are now allowed to use interval numbers and type-1 and type-2 fuzzy sets to intuitively and quantitatively express their diverse knowledge and experience, which will then be processed by the new theory to form fuzzy state vectors and fuzzy event transition matrices. Accordingly, we have established mathematical operations that cover the computations of fuzzy states, fuzzy event transitions, and parallel composition. Numerical examples are provided.     

Diagnosability analysis of hybrid systems cast in a discrete-event framework

This paper addresses the problem of assessing the diagnosability of hybrid systems modeled by a hybrid automaton coupling methods from the continuous and the discrete event model-based diagnosis fields. The discrete states of the hybrid automaton represent the modes of operation of the system for which the continuous dynamics are specified. The diagnosability of the continuously-valued part of the model is first analyzed and the new concept of mode signature is shown to characterize mode diagnosability from continuous measurements. Continuous dynamics are then abstracted by defining a set of signature-events associated to mode signature changes, preserving this way mode diagnosability. The behavior of the abstract hybrid system is then modeled by a prefix-closed language over the original event alphabet enriched by these additional events. Based on this language, diagnosability analysis of the hybrid system is cast into a discrete-event framework and hybrid diagnosability conditions are provided. A case study based on the Attitude and Orbit Control System of a spacecraft illustrates the method.