The emergence of computational intelligence technology inspired by biological and human intelligence is one of the most exciting and important fields in engineering . It is expected that these technologies like fuzzy logic , will play a significant role in the development of intelligent robotic systems , machine systems , and mechatronics systems . In this work ; a Fuzzy Logic Controller is presented to control the motion of a robotic arm and avoid the obstacles existed in its mission road ,using a real platform robotic arm in combination with a vision system . This work involves constructing an integrated and autonomic MATLAB program. It could be applicable for any robotic arm . It depends on a new approach in analyzing the robotic environment videos acquired by a fixed webcam. The approach uses colors to detect and recognize the changeable locations and objects’ dimensions for each of the robot’s end-effector , the goal , and the obstacles .
Main aim of this book is to identify, model and control robotic manipulator with three degrees of freedom. The book is a part of major project , the aim of which is to create an educational platform. In the book the simple PID control and the PID with feedforward compensation control is tested on the model of simple pendulum. In the next part models of DC motors, which are used for construction of the manipulator, are developed and the inverse dynamics model of manipulator is developed. This model is used for feedforward control of the manipulator. In the final part the application was developed, which allows the manipulator to be taught some movements, which can be later on, executed. For the simple control of the application the graphical user interface was programmed.
Design and implementation of fuzzy logic controller for mobile robot navigation in unknown environments is presented. The task of navigation is divided into three behaviors namely hurdle avoidance, wall following and goal seeking. The outputs from these behaviors are combined to generate collision free motion of robot amongst obstacles in reaching the target. The controllers for these behaviors are designed using Fuzzy Logic toolbox of MATLAB® and their implementation is realized with readily available and inexpensive AT89C52 microcontrollers. Finally, the robot with these controllers is tested in indoor environments containing obstacles with changing destination places and is found to reach the set targets successfully which shows the validity of the designed controllers in achieving the required task.
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.
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.
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.
Although robotic arms for power wheelchairs have been developed, existing models have limitations for people who cannot use joystick controls or who need to maneuver in small spaces. This book addresses these problems by developing programs to link brain computer interface communication systems to robotic controls and by designing a smaller and more compactable robotic manipulator. Finally, the success and possible implications of using a BCI unit with a redundant manipulator robotic arm was examined during the reflections of this book. The program developed to communicate the BCI unit with the redundant robotic manipulator was also developed and reflected upon as well as the smaller design of an existing redundant robotic manipulator for a power wheelchair.
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.
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.
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.
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.
Fuzzy logic which was first introduced by Lotfi A. Zadeh in 1965, transcends the “black and white” approach of the Aristotelian logic, and tries to capture the wide grey areas of imprecision in between. This logic provides a mathematical approach to interpret the grey zones of imprecision. Some authors consider EBM and Fuzzy logic as two sides of a same coin. We believe that fuzzy logic approach is not in contrast to the EBM approach, but it is a complementary tool for a more realistic approach to the practice of evidence-based medicine. By merging and concurrent use of these two approaches (EBM and Fuzzy logic) in the process of decision-making, fuzzy logic can yield its suitable place in the field of medicine, considering the rapidly progression of EBM in this field. In this book we are trying to report our experience in integrating fuzzy logic in the decision process of VTE prophylaxis in hospitalized patients according to an Evidence-based risk assessment model (RAM), developed by Joseph A. Caprini. We believe that the application of Fuzzy thinking model in developing decision support systems can resolve most of the shortcomings of RAMs & practice guidelines.
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.
One of the main and recent problems in Malaysian hospitals is the lack of surgeonsand specialists, especially in rural areas. Insufficient specialised surgeons in such regions particularly in the niche of orthopaedic causes more fatalities and amputees due to time constrain in attending the patients. Broken limbs due to accidents can be treated and recovered. But severed blood vessels results in blood loss and leads to amputation or even worst fatalities. A mobile robotic system known as OTOROB is designed and developed to aid orthopaedic surgeons to be virtually present at such areas for attending patients. The developed mobile robotic platform requires a flexible robotic arm vision system to be controlled remotely by the surgeon. To be present virtually is still insufficient if clearer view is not obtained. Thus, a flexible robotic arm with vision system as end effector is designed, developed and tested in real time. Fuzzy logic is implemented in the control system to provide safety for the robotic arm articulation. The safety systems of the robotic arm consist of Danger Monitoring System (DMS), Obstacle Avoidance System (OAS) and Fail Safe and Auto Recovery System (FSARS).
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.