Linear defuzzified output of fuzzy controllers via mixed fuzzy logic operators

Abstract

The linear defuzzified output of a fuzzy controller with two fuzzy variable inputs and one output is discussed in this paper. Arbitrary amounts of triangular fuzzy numbers are employed to fuzzify the linguistic variables in fuzzy control rules. We show that the defuzzified output is exactly equivalent to a linear function of the inputs to the fuzzy controller by using three mixed fuzzy logic operators to evaluate the control rules.     

一种实用的自组织模糊控制器设计方法

介绍一种基于模糊智能比例因子自调整的自组织模糊控制器设计方法 ,给出这种模糊控制器与常规模糊控制器在性能指标上的比较结果 .     

A fuzzy logic based approach to reliability improvement estimation during product development

During early stages of product development process, a vast amount of knowledge and information is generated. However, most of it is subjective (imprecise) in nature and remains unutilized. This paper presents a formal structure for capturing this information and knowledge and utilizing it in reliability improvement estimation. The information is extracted as improvement indices from various design tools, experiments, and design review records and treated as fuzzy numbers or linguistic variables. Fuzzy reasoning method is used to combine and quantify the subjective information to map their impact on product reliability. The crisp output of the fuzzy reasoning process is treated as new evidence and incorporated into a Bayesian framework to update the reliability estimates. A case example is presented to demonstrate the proposed approach.     

A fuzzy logic approach to an integrated maintenance/production scheduling algorithm

Scheduling algorithms play an important role in manufacturing systems as a means of meeting customer demands. On the other hand, fuzzy logic, which has been successfully implemented in many engineering applications, including the recent work of Vanegas and Labib (2001a,b), has an ability to produce a more gradual transition. This paper presents an algorithm for transforming maintenance data to shop floor information. These shop floor data are then used via a fuzzy-logic based scheduling algorithm to determine optimal production systems control policies. The frequency of breakdowns and the mean number of parts required are used as inputs to the fuzzy logic controller. These inputs are transformed to the mean part arrival rate. The output is then fed to the scheduling algorithm. Finally, the optimal batch size is calculated. The algorithm is demonstrated with simulation.     

Constrained mathematics evaluation in probabilistic logic analysis

A challenging problem in mathematically processing uncertain operands is that constraints inherent in the problem definition can require computations that are difficult to implement. Examples of possible constraints are that the sum of the probabilities of partitioned possible outcomes must be one, and repeated appearances of the same variable must all have the identical value. The latter, called the ‘repeated variable problem’, will be addressed in this paper in order to show how interval-based probabilistic evaluation of Boolean logic expressions, such as those describing the outcomes of fault trees and event trees, can be facilitated in a way that can be readily implemented in software. We will illustrate techniques that can be used to transform complex constrained problems into trivial problems in most tree logic expressions, and into tractable problems in most other cases.     

A fuzzy logic modelling of dynamic scheduling in FMS

This paper is concerned with scheduling in flexible manufacturing systems (FMSs) using a fuzzy logic (FL) approach. Four fuzzy input variables: machine allocated processing time, machine priority, due date priority and setup time priority are defined. The job priority is the output fuzzy variable, showing the priority status of a job to be selected for next operation on a machine. The model will first select the machines and then assign operations based on a multi-criteria scheduling scheme. The performance of the approach is compared against established methods reported in the literature. The performance measures considered average machine utilisation, meeting due dates, setup times, work in process and mean flow times. The test results demonstrate the superiority of the fuzzy logic approach in most performance measures.     

一种基于模糊逻辑的带宽管理方法

在网络带宽受限的网络控制系统中,设计了一个基于一维输入输出映射关系的模糊逻辑调节器,对 控制系统的带宽进行动态管理。通过调整该模糊逻辑调节器中的非线性偏移因子可以进一步改善系统的控 制性能和带宽需求。同时利用线性矩阵不等式及资源受限条件来确定带宽动态分配的上下界来保证系的 稳定性。另外,定义了多回路归一化控制质量和归一化带宽需求度性能衡量指标来评价网络控制系统的全 局性能。在此评价方法下,提出的模糊带宽管理方法和采用固定带宽分配方法进行仿真比较。结果表明模 糊带宽管理方法相对于固定带宽分配方法而言在提高系统控制性能的同时也节省了网络带宽。     

On fuzzy and probabilistic control charts

In this article, different procedures of constructing control charts for linguistic data, based on fuzzy and probability theory, are discussed. Three sets of membership functions, with different degrees of fuzziness, are proposed for fuzzy approaches. A comparison between fuzzy and probability approaches, based on the Average Run Length and samples under control, is conducted for real data. Contrary to the conclusions of Raz and Wang (1990b) the choice of degree of fuzziness affected the sensitivity of control charts.     

Modelling and verification of program logic controllers using timed automata

Validation is an important task in complex embedded system designs. A method of modelling and analysing embedded systems with programmable logic controllers is presented. Controllers and physical plants are modelled using timed automata. The system requirements are specified and formalised as computational tree logic properties. It is demonstrated that the designed model satisfies the required properties by resorting to a symbolic model checker, Uppaal, for real-time systems. A realistic example, the steeve controller of a theatre, illustrates the strategies. The safety and time constraint requirements are validated by Uppaal. The experimental results demonstrate the effectiveness of the approach presented here.     

Failure experience of programmable logic controllers used in emergency shutdown systems

Microprocessor-based programmable logic controllers (PLCs) are often used in applications to safety shut down a process or facility if unsafe conditions are detected. Increased use of quantitative reliability analysis requires additional and more detailed failure rate estimates for analyzing PLCs. This technical note presents failure rates derived from PLC performance in emergency shutdown (ESD) systems for natural gas compressor stations. The PLCs analyzed operate with roughly 40 inputs processed through three input modules and generate approximately 20 control signals through two output modules. Failure rates range from 0·072/year when the PLC alone was considered to 0·32/year when all types of failures were considered. (All failures include effects that are not necessarily attributable to the PLC microprocessors, such as human errors while working in the PLC cabinets and failures of common power supplies.)     

一种瓦楞纸板生产线原纸张力模糊控制系统

现行的瓦楞纸板生产线原纸张力是人工调节原纸轴架的动力实现的,因调节不连续而影响产品质量的稳定性,且常断纸而造成原纸的较大损耗,为此研制了一种新型的张力模糊控制系统,能在整个生产过程中进行自动恒张力控制,在此介绍了该系统的控制原理、硬件组成和软件主要功能及控制策略。     

Applying fuzzy logic and constraint networks to a problem of manufacturing flexibility

The primary contribution is to present an application of fuzzy logic and constraint networks to a problem of manufacturing flexibility. The paper begins with a literature review showing the different approaches when measuring manufacturing flexibility. Next, it provides a brief review of fuzzy logic and its applications, explaining how it enhances the ability to model flexibility strategies. Then, the application is presented and its utility is demonstrated with an example from the production of printed circuit boards. Finally, the paper concludes with comments on how this model could be expanded to other applications.     

Ranking sensors using an adaptive fuzzy logic algorithm

A procedure to rank sensors according to their noise rates was developed based on an adaptive fuzzy logic algorithm for sensor fusion. No a priori knowledge of the sensors performance is assumed. Simulation analysis indicated 83.33% successful ranking with noise rates up to 50%. In an indoor experiment with a mobile robot equipped with three logical sensors, 88% of the rankings were correct. The ranking procedure also indicates the ranking results success probability.     

基于Haar小波和模糊逻辑的纸币图像特征提取方法

针对如何提取纸币图像特征的问题,提出了一种基于离散Haar小波变换和模糊逻辑相结合的纸币特征提取方法。该方法首先使用Haar小波对纸币图像进行分解操作,提取出图像的低频小波系数、高频小波系数。在此基础上引入模糊逻辑方法,把提取的小波系数分别作为语言变量,并构造出相应的隶属度函数,在模糊特征空间中求出每个模糊区域对应的激活强度值,将这些激活强度值进行归一化处理后构成纸币特征向量,使用神经网络分类器对纸币进行识别。此方法在资源约束的嵌入式系统(TI TMS320C6713 DSP)上实现,实验结果表明,离散Haar小波变换和模糊逻辑相结合的特征提取方法可以取得较高的识别率。     

Modeling ductile to brittle transition temperature of functionally graded steels by fuzzy logic

In this article, a model based on fuzzy logic (FL) for predicting ductile to brittle transition temperature of functionally graded steels in both crack divider and crack arrester configurations has been presented. Functionally graded steels containing graded ferritic and austenitic regions together with bainite and martensite intermediate layers were produced by electroslag remelting. For purpose of building the model, training and testing using experimental results from 140 specimens produced from two basic composites were conducted. The used data as inputs in FL models are arranged in a format of six input parameters that cover the FGS type, the crack tip configuration, the thickness of graded ferritic region, the thickness of graded austenitic region, the distance of the notch from bainite or martensite intermediate layer, and temperature. According to these input parameters, in the FL, the ductile to brittle transition temperature of each FGS specimen was predicted. It has been found that FL model will be valid within the ranges of variables. The training and testing results in the FL model have shown a strong potential for predicting the ductile to brittle transition temperature of each FGS specimen.     

Human performance reliability: on-line assessment using fuzzy logic

This paper presents an on-line approach to monitoring human performance in terms of conditional reliability when one is performing a task. Unlike traditional human reliability analysis, this approach develops a dynamic model that is able to cope with constantly changing conditions that affect operator performance. A fuzzy knowledge-based assessment approach is developed in order to deal with uncertainty and subjectivity associated with human performance assessment. This technology includes three main parts/functions: (i) on-line performance monitoring; (ii) real-time performance forecasting; and (iii) performance reliability assessment. The technology is demonstrated in real-time and provides timely conditioned reliability information regarding task success/failure. In general, this technology offers human reliability assessment under highly dynamic circumstances.     

Production scheduling using adaptable fuzzy logic with genetic algorithms

Production scheduling for a flexible manufacturing environment must satisfy multiple conflicting criteria. Whilst estimation and modelling of capacity is facilitated by commercially available tools, the actual release strategy of orders into the system is still subject to considerable research as improved solutions over conventional dispatching heuristics are sought. An order release mechanism incorporating an adaptable fuzzy logic system enhanced by genetic algorithms is proposed. Through the use of fuzzy logic, the system can consider multiple criteria and rapidly determine solutions of consistently high quality. Adaptability ensures that the solution quality is maintained throughout the life of the system. The subsequent application of a genetic algorithm follows an efficient optimization path, since the initial solution derived through fuzzy logic is known to be good. The system developed, using the combined methodology, was tested on a discrete event simulation model and showed measurable benefits in schedule performance against commonly implemented dispatching heuristics.     

Security risk assessment: Applying the concepts of fuzzy logic

Chemical process industries (CPI) handling hazardous chemicals in bulk can be attractive targets for deliberate adversarial actions by terrorists, criminals and disgruntled employees. It is therefore imperative to have comprehensive security risk management programme including effective security risk assessment techniques. In an earlier work, it has been shown that security risk assessment can be done by conducting threat and vulnerability analysis or by developing Security Risk Factor Table (SRFT). HAZOP type vulnerability assessment sheets can be developed that are scenario based. In SRFT model, important security risk bearing factors such as location, ownership, visibility, inventory, etc., have been used. In this paper, the earlier developed SRFT model has been modified using the concepts of fuzzy logic. In the modified SRFT model, two linguistic fuzzy scales (three-point and four-point) are devised based on trapezoidal fuzzy numbers. Human subjectivity of different experts associated with previous SRFT model is tackled by mapping their scores to the newly devised fuzzy scale. Finally, the fuzzy score thus obtained is defuzzyfied to get the results. A test case of a refinery is used to explain the method and compared with the earlier work.     

Evaluation of leagility in supply chains using fuzzy logic approach

In the changing business environment, manufacturing firms can survive by catering to the dynamic demands of the modern customers. Lean principles imply zero inventory and agile principles necessitate safety inventory to tackle volatile market conditions. The leagile paradigm is gaining importance in the contemporary scenario which includes both lean and agile principles. This article presents the conceptual model of leagility imbibed with lean and agile principles. A fuzzy logic approach has been used for the evaluation of leagility in supply chains. This article is used to compute the performance of supply chains using both lean and agile concepts as leagility supply chains using a fuzzy logic approach.     

An approach of fuzzy logic evaluation and control in SPC

Quality control plays an important part in most industrial systems. Its role in providing relevant and timely data to management for decision‐making purposes is vital. A method that uses statistical techniques to monitor and control product quality is called statistical process control (SPC), where control charts are test tools frequently used for monitoring the manufacturing process. Engineers or managers can evaluate an abnormal process by using SPC zone rules in control charts. In the conventional use of the zone rules the user is only able to determine whether or not the process is out of control. What action should be taken to adjust the process is uncertain and is evaluated based on knowledge of the system and past experiences. This paper explores the integration of fuzzy logic and control charts to create and design a fuzzy–SPC evaluation and control (FSEC) method based on the application of fuzzy logic to the SPC zone rules. A simulation program implementing FSEC was written in Borland C++ 5.0 and simulation results were obtained and analysed. The abnormal processes simulated were automatically adjusted for each of the zone rules tested and showed an improved performance after the control action, thus confirming the merit of the technique as a special method with the specific numerical control action based on a quality evaluation criterion. Copyright © 2000 John Wiley & Sons, Ltd.