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.
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.
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.
The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for several nerual netwrok and fuzzy algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed, includign elastic fuzzy control. An experimental method for determining describing function of SISO fuzzy controller is given as well. The work described in this book serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of adaptive and neural network control systems, as well as for the analysis of the different algorithms such as elastic fuzzy systems.
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 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.
In this book, two types of direct adaptive control schemes for a class of nonlinear systems are proposed. Based on the feedback linearization theory, the architecture employs for the first approach the fuzzy logic reasoning of Takagi Sugeno (TS) type and uses for the second approach the strategy of neural network reasoning of radial basis function (RBF) type to approximate the feedback linearization control law. In each case, the parameters of the adaptive controller are adapted according to a law derived using Lyapunov stability theory. The adaptive controller is applied in simulation to control three nonlinear systems in both the fuzzy and the neural network methods.
A number of academic and industrial researches incontrol systems have exposed the inherent weaknessesof PID control which are; rigidity, prohibitivecomputational complexity and non-applicability forintelligent and complex systems. Consequently, agroup of researchers have proposed fuzzy logiccontrol as a better alternative to PID control. Thisnotion has spawned numerous debates amongresearchers, experts and professionals in the fieldof control systems. As a result, this bookinvestigates and compares the performance oftraditional control techniques with fuzzy logiccontrol which will be optimized and made adaptive tothe variations of the sensor input. It will also beproven that fuzzy logic control is far more superiorin performance to the existing traditional controltechniques. These objectives were achieved throughthe use of MATLAB and SIMULINK to simulate, tweak andfine-tune the different cases for the response andtheir respective performance metrics. Interestinglyas expected, the results of the simulations showsthat fuzzy logic control, optimized or not, is betterthan the traditional control techniques, especially,PID control.
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.
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.
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.
These are exciting times in the fields of Fuzzy Logic and the Semantic Web, and this book will add to the excitement, as it is the first volume to focus on the growing connections between these two fields. This book is expected to be a valuable aid to anyone considering the application of Fuzzy Logic to the Semantic Web, because it contains a number of detailed accounts of these combined fields, written by leading authors in several countries. The Fuzzy Logic field has been maturing for forty years. These years have witnessed a tremendous growth in the number and variety of applications, with a real-world impact across a wide variety of domains with humanlike behavior and reasoning. And we believe that in the coming years, the Semantic Web will be major field of applications of Fuzzy Logic.This book, the first in the new series Capturing Intelligence, shows the positive role Fuzzy Logic, and more generally Soft Computing, can play in the development of the Semantic Web, filling a gap and facing a new challenge. It covers concepts, tools, techniques and applications exhibiting the usefulness, and the necessity, for using Fuzzy Logic in the Semantic Web. It finally opens the road to new systems with a high Web IQ.Most of today's Web content is suitable for human consumption. The Semantic Web is presented as an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. For example, within the Semantic Web, computers will understand the meaning of semantic data on a web page by following links to specified ontologies. But while the Semantic Web vision and research attracts attention, as long as it will be used two-valued-based logical methods no progress will be expected in handling ill-structured, uncertain or imprecise information encountered in real world knowledge. Fuzzy Logic and associated concepts and techniques (more generally, Soft Computing), has certainly a positive role to play in the development of the Semantic Web. Fuzzy Logic will not supposed to be the basis for the Semantic Web but its related concepts and techniques will certainly reinforce the systems classically developed within W3C.In fact, Fuzzy Logic cannot be ignored in order to bridge the gap between human-understandable soft logic and machine-readable hard logic. None of the usual logical requirements can be guaranteed: there is no centrally defined format for data, no guarantee of truth for assertions made, no guarantee of consistency. To support these arguments, this book shows how components of the Semantic Web (like XML, RDF, Description Logics, Conceptual Graphs, Ontologies) can be covered, with in each case a Fuzzy Logic focus.Key features.- First volume to focus on the growing connections between Fuzzy Logic and the Semantic Web.- Keynote chapter by Lotfi Zadeh.- The Semantic Web is presently expected to be a major field of applications of Fuzzy Logic.- It fills a gap and faces a new challenge in the development of the Semantic Web.- It opens the road to new systems with a high Web IQ.- Contributed chapters by Fuzzy Logic leading experts.- First volume to focus on the growing connections between Fuzzy Logic and the Semantic Web.- Keynote chapter by Lotfi Zadeh.- The Semantic Web is presently expected to be a major field of applications of Fuzzy Logic.- It fills a gap and faces a new challenge in the development of the Semantic Web.- It opens the road to new systems with a high Web IQ.- Contributed chapters by Fuzzy Logic leading experts.
The main part of the book is a comprehensive overview of the development of fuzzy logic and its applications in various areas of human affair since its genesis in the mid 1960s. This overview is then employed for assessing the significance of fuzzy logic and mathematics based on fuzzy logic.
Neural networks and fuzzy logic have some common features such as distributed representation of knowledge, ability to handle data with uncertainty and imprecision etc. Fuzzy logic has tolerance for imprecision of data, while neural networks have tolerance for noisy data. A neural network’s learning capability provides a good way to adjust expert’s knowledge and it automatically generates additional fuzzy rules and membership functions to meet certain specifications. This reduces the design time and cost. On the other hand, the fuzzy logic approach enhances the generalization capability of a neural network by providing more reliable output when extrapolation is needed beyond the limits of the training data. To simplify the design and optimization of fuzzy models, process learning techniques derived from neural networks (so called neuro-fuzzy approaches) can be used.Different architectures of neuro-fuzzy systems have been discussed by a number of researchers. In this research, neuro-fuzzy system networks are deployed and used for the dynamic modeling of a nonlinear MIMO system (heat recovery steam generator (HRSG)) and a permeability prediction process.