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 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 .
The authors guide readers quickly and concisely through the complex topics of neural networks, fuzzy logic, mathematical modelling of electrical machines, power systems control and VHDL design. Unlike the academic monographs that have previously been published on each of these subjects, this book combines them and is based round case studies of systems analysis, control strategies, design, simulation and implementation. The result is a guide to applied control systems design that will appeal equally to students and professional design engineers. The book can also be used as a unique VHDL design aid, based on real-world power engineering applications.*Introduces cutting-edge control systems to a wide readership of engineers and students*The first book on neuro-fuzzy control systems to take a practical, applications-based approach, backed up with worked examples and case studies*Learn to use VHDL in real-world applications
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 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.
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 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.
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.
There has been a significant increase in the application of Artificial Intelligence(AI) to many practical problems in recent years. Fuzzy Logic has been one of the major tools in the application of AI. This book involves the designing of an Anti-Locking Braking System that controls the speed of any vehicle and hence can be used for transportation applications. Fuzzy Logic controller provides an alternative to controller since it is a good tool for control of systems that are difficult in modeling. Audience for this book This book is intended for the following audience: * Computer Science and Engineering (CSE), Information Technology, Electronics and Communication Engineering students who would like to specialize in Embedded Systems Automotive Electronics). The book should be appropriate for third year students who do have a basic knowledge of computer hardware and software. This book can also be used for the final year students for their User Defined Problem (UDP) * Engineers who have so far worked on systems hardware and who have to move more towards software of embedded systems. This book should provide enough background to understand relevant technical publication
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.
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.
As an academic text, the book is filled with practical design examples, discussions and problems. We employed Fuzzy Logic system to control and monitor different problems of Physics including prediction of volcanic activity, detection of radon concentration, forecasting cyclone intensity and detecting the concentration of hydrogen cyanide.Fuzzy Logic has been found to be very suitable for embedded control application. This incorporates an alternative way of thinking, which allows modeling complex systems using a higher level of abstraction originating from our knowledge and experience.Recent year have witnessed of tremendous advances in wirelessly networked and embedded sensors. Wireless sensor nodes are typically low cast, low power, small devices equipped with limited sensing, data processing and wireless communication capabilities.
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.
Continuous stirred tank reactor is a typical chemical reactor with complex non linear dynamic characteristics. There has been considerable interest in its state estimation and real time control based on mathematical modeling. However, the lack of understanding of the dynamics of the process, the highly sensitive and non linear behavior of the reactor, has made it difficult to develop the precise mathematical modeling of the system. Efficient control of the product concentration in CSTR can be achieved only through accurate model. Here attempts are made to ease the modeling difficulties using AI techniques such as Fuzzy logic. Simulation results demonstrate the effectiveness of Fuzzy logic control technique. The system is a stirred tank reactor with two flows of liquid enter the system and another flow exits the tank with the formation of product. Simulink was employed to design a model for this system in the simulation environment. This work is aimed for utilization by researchers, industrialists’ and students. Furthermore it will be especially relevant and useful for the students of Electrical Engg. and Chemical Engg. for carrying out research in the area of simulation of CSTR.