Over the last few decades industrial automation has become the most desirous subject to increase the productivity with quality and quantity enhancement and consider the cost management of the product. A new approach of Fuzzy Logic Time Control Discrete Event System DEV provides an opportunity to establish a control design with certain time constraint of activation under the fuzzy control of input variables. A multi-agent based approach helps the control strategy to be motivated with all internal and external factors effecting the system casually under un predetermined conditions.An approach to use the fuzzy system in local and distributed environment is explored using a simplified design algorithmic approach.Design Models of: Liquids Mixing System,Grinding and Mixing System, and Muti- Dimensional Supervisory Control System with Fuzzy Logic time control DEV strategy are explored for industrial automation.
Petri nets are considered to be the alternative approach for analysis, design and implementation of DES. However, the prospect of uncertain and vague information still creates a problem. Hence, to handle the uncertainty, fuzzy logic is integrated into Petri nets to form Fuzzy Automation Petri nets as a modeling tool to handle these uncertainties. The use of Fuzzy Automation Petri nets (FAPN) demands a valid translation of the formalism to Ladder Logic Diagrams for implementation. This is achieved using Token Passing Methodology. This work investigates the use of Fuzzy Petri nets in supervisory control and suggests a modified and improved version called Fuzzy Automation Petri net (FAPN) as a modeling tool. It presents a systematic approach to the synthesis of Fuzzy Petri net based supervisor for the forbidden state problem using supervisory design procedure. The controlled model of the system can be constructed from this FAPN net structure. The implementation is using Flexible Manufacturing System as an example of DES.
This study is aimed at characterization of Fuzzy logic control based system with varying span of Member Function by establishing region of stable operation for different cases using range provided by 'k' for setting the span for desired performance.The performed work may be used as reference, by designers for designing a Fuzzy logic based control system depending upon the requirement of desired application.
Fuzzy controllers are used to control consumer products, such as washing machines, video cameras, and rice cookers, as well as industrial processes, such as cement kilns, underground trains, and robots. Fuzzy control is a control method based on fuzzy logic. Just as Fuzzy logic can be described simply as “computing with words rather than numbers’’ and Fuzzy control can be described simply as “control with sentences rather than equations’’. A Fuzzy controller can include empirical rules, and that is especially useful in operator controlled plants.This is the world of automation. The majority of the products allow actions to be automatically triggered by events. The Performance of a SCADA system can be much improved using a fuzzy logic controller based SCADA in industries. This book describes design of fuzzy logic based SCADA Systems using MATLAB fuzzy logic toolbox.This Book is useful for Engineers and Research Scholars in the field of Electrical & Power Engineering.
This book presents the details theory and applications of Fuzzy sets,fuzzy systems,membership functions & controller designed. A logic based on the two truth values True and False is sometimes inadequate when describing human reasoning. Fuzzy logic uses the whole interval between 0 (False) and 1 (True ) to describe human reasoning. As a result, fuzzy logic is being applied in rule based automatic controllers. A fuzzy control method for automatic steering and a method for line tracking are conveyed in this article. The principal for fuzzy control steering and the construction of fuzzy controller are described in detail.For Example,A vehicle is refitted with a storage battery car. Navigation control system is developed based on digital compass and other sensors. The vehicle acquires the location information using the fuzzy control steering and line tracking method. Test results indicated that the navigation lateral error was less than 0.3m when the robot ran following the predefined line route. Finally, a power system network with UPFC has been considered & A POD controller has designed by using Fuzzy Logic to improve the damping oscillation of power system network.
The design of controllers for non- linear systems in industry is a complex and difficult task. The development of non-linear control techniques has been approaches in many different ways with varied results. One approach, which has shown promise for solving nonlinear control problems, is the use of fuzzy logic control. This book will discuss the Magnetic Levitation (Maglev) models as an example of nonlinear systems. It will also show the design of fuzzy logic controllers for this model to prove that the fuzzy controller can work properly with nonlinear system. Genetic Algorithm (GA) is used in this book as optimization method that optimizes the membership, output gain and inputs gain of the fuzzy controllers. Finally, fuzzy controller will be implemented using FPGA chip. The design will use a high-level programming language HDL for implementing the fuzzy logic controller using the Xfuzzy CAD tools to implement the fuzzy logic controller into HDL code. This book is designed for the professional and academic audience interested primarily in applications of fuzzy logic in engineering and technology.
With every year air conditioning units are increasing rapidly all over the world and air conditioning system has not only become integral part of every institution but also of our lives. The survey of this system shows that air conditioning system consumes nearly 50% of the total energy intake of a building. In air conditioning system, compressor unit consumes most of the energy. Different control techniques can be used to control the compressor speed. This book therefore provides the use of two intelligent control techniques to control the compressor speed and make air conditioning system energy efficient. The techniques used are fuzzy logic and neuro-fuzzy. This book also provides the comparison of both the techniques. This book should be helpful for professionals in artificial intelligence, or anyone else who may seek to work in Fuzzy , Neuro-fuzzy or related techniques. This book would also be helpful to students for study of these techniques.
This research work is mainly focused on suspending the steel ball without any mechanical support in desired position and how the Magnetic Levitation System works in presence of disturbance with help of an efficient controller.The tracking performance and robustness is also checked for this system. For tracking, two type of reference trajectory are modelled. One is sine wave and other is a set of constant point varying at different levels. Lastly for robust performance, disturbance is applied in MLS.For this task we have designed Interval Type-2 Fuzzy Logic Controller (IT2FLC), Interval Type-2 Single Input Fuzzy Logic Controller (IT2SIFLC), Interval Type-2 Fuzzy Sliding Mode Controller (IT2FSMC) based on theory of type-2 fuzzy logic systems. Uncertainty is an inherent part of intelligent systems used in real world applications. Conventional controllers can not fully handle the uncertainties present in real-time systems. Type-2 fuzzy sets that are used in type-2 fuzzy systems can handle such uncertainties in a better way because they provide us an extra degree of freedom. At last The designed controller’s performance is compared with the feedback linearization control.
Coordinated traffic signals regulate traffic flow through urban areas and minimize delay and maximize traffic flow. In this study an attempt is made to design coordinated traffic signal using the concept of fuzzy logic. Fuzzy logic is very effective in incorporating those parameters which have an inherent fuzziness in them like the user perception. Coordinated signal is designed using fuzzy logic by formulating fuzzy rules relating the qualitative parameter (Quality of progression) and other important quantitative parameters like average stream speed, V/C ratio and average control delay .Measures of effectiveness like average control delay per vehicle per cycle length, band width and efficiency are used to determine the efficiency of signal coordination. It was found that design using Fuzzy logic concept is more efficient and can also account for the dynamic traffic flow conditions.
Fuzzy Logic Control (FLC) is an important alternative method to the conventional Proportional, Integral, and Derivative (PID) control method for use in nonlinear systems. This book, therefore, highlight the feasibility and effectiveness of fuzzy logic control in application to mathematical models of two basic types of steam turbines; straight expansion and single-automatic extraction turbines. The derived performance of the developed mathematical models, in terms of input/output duty variables without mean of control, is found to be in a good agreement with the actual performance of typical steam turbines with practical technical data and operating conditions. Model components exhibit nonlinear behavior. A comparison is made between the efficiency of Fuzzy Logic Control and the conventional PID control for the dynamic responses of the closed loop drive system. In case of straight expansion steam turbines, the control task is either speed or backpressure control. In case of single extraction steam turbines, the control task is to maintain both speed and extraction pressure of the turbine constants. This is done in presence of severe changes in load and/or steam demand conditions.
A very convenient mechanism for exploitation of uncertainty and vagueness in decision making is fuzzy logic. This paper shows practical application of fuzzy logic in the Armed Forces of Serbia through an example of selecting the location for setting a bridge for crossing a water obstacle. By the fuzzy logic system inexperience of the decision maker is minimized. This demonstration is even more significant considering experience of some experts gained during combat activities. The criteria relevant for the choice of a bridge crossing point, as well as their influence on the choice of the alternatives, have the values displayed in numerical values or fuzzy linguistic descriptors. By analyzing the results obtained, we can conclude that the developed fuzzy system can successfully evaluate the chosen locations and formulate the strategy of decision-making in the choice of the location. This book should hlep shed some light on modelling of fuzzy logic system to support decision making process, and should be especially useful to students who have interest in fuzzy logic modelling, or anyone else who have interest in operational researchs.
Discrete Event Frameworks of Environmental Sustainable Development expounds upon an important chapter of artificial intelligence; respectively, discrete event systems applied for modeling and simulation of control, logistic supply, chart positioning, conservation and protection of natural resources in order to have a clean and healthy environment capable to ensure a sustainable development of modern global society. All these factors allow for a new design of artificial social systems dotted with intelligence, autonomous decision-making capabilities, and self-diagnosing properties.
Fuzzy logic technics are not always fuzzy in technology because they are being used in electrical power stability studies to make life better. This happens because real time mathematical analysis can be substituted with real life decision making process using Fuzzy logic technics. The motivation to use fuzzy logic technics is based in the fact that many everyday technologies in the living room such as air conditioning,refridgerators, control of light and microwaves have been developed using fuzzy logic technics. Above all it has been proved by Japanese engineers that trains controlled using Fuzzy logic technics have better and smooth ride for passengers than those controlled using other methods.
The research presented examines the construction of a fuzzy logic controller for complex nonlinear system by control system decomposition into hierarchical fuzzy logic sub-systems. Evolutionary algorithm based methods are proposed to determine the control system for the hierarchical fuzzy system (HFS). Different HFS topologies for a given dynamical system (such as the inverted pendulum system) are investigated. For the inverted pendulum system, a single layer, two layered, three layered, and four layered HFS, with different variable input configuration is investigated. Effects of different input configurations on controller performance are examined and discussed. A new evolutionary algorithm based compositional method is proposed to control system over the whole set of user-defined initial conditions. The method addresses directly the problem of controlling the dynamical system from specific, user-defined initial conditions. The multiobjective evolutionary algorithm (MOEA) based compositional method is developed and tested on the example of the inverted pendulum system.
Information retrieval system is heart of information system. The primary purpose of establishing an information retrieval system lies in assisting the users to effectively acquire drsired information. That is, users' query must be properly understood and answered. The present work falls in the area of information retrieval and to be more specific : query processing of information retrieval. This has been influenced by the limitation and disadvantages of the commercially available Boolean logic retrieval model. The limitation and disadvantages of the query processing of the Boolean logic model have been pointed out and logical solution using fuzzy set theory and fuzzy logic have been presenteted.