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 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.
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 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.
PI or PID based control system are widely used in control process industries because of their implantation is simple and they assure acceptable performance for industrial processes and their can be tuned manually by industrial operators. However, these controllers provide better performance only at given set of operating range and they need to be redefine if there is change in operating conditions. Further, the conventional controller performance is not up to the expected level for nonlinear and dead time processes. In the present industrial scenario, all the processes require automatic control with good performance over a wide operating range with simple design and implementation. This provides us the motivation for development of Fuzzy logic based process control system which can control process efficiently for all practical operating conditions. The fuzzy logic has been used to control the air pressure in the vessel using matlab as programming platform and the results are compared with that PID control. It has been demonstrated that fuzzy logic based control system is more accurate than the PID control system.
This book explains the basic principle underlying in system/model identification.The model-based control design process involves modeling the plant to be controlled, analyzing and synthesizing a controller for the plant, simulating the plant and controller, and deploying the controller here we concentrated only on designing the controller.A comprehensive chapter which clearly explain about model structures,sugar evaporator,Fuzzy controller.A detailed work and performance analysis on sugar evaporator with conventional controllers P, PI, PID & Fuzzy logic Controller results were displayed and implemented.
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.
The Book Modeling Quality of Service (QoS) in a Telecommunications Network using Neuro-fuzzy logic, this is generally the analysis of GSM theory as a telecommunications network and ANFIS as a non-linear modeling tool. The ANFIS-based model developed has demonstrated that it can be used to model Quality of Service using Logical Control and Traffic Channels key performance indicators, due to its degree of consistency. The statistical analysis has further ascertained and confirmed the accuracy of the ANFIS-based model developed.
In this book, we develop an idea around a type of control system based on fuzzy logic to help a driver maintain the kinetic balance of the vehicle. After a brief discussion on the vehicle dynamics stability theory in the first chapters, we introduce the main idea together with simulation results and hardware implementation. The main idea is to investigate the vehicle dynamics instability based on the fuzzy analysis of the vehicle CG (center of gravity) behavior and to use sensors and actuators in order to coordinate suspension, brakes and steering system in critical situations. An advantage of this controller is that it doesn''t interfere with the driver''s habit in vehicle control and it resumes functioning only in critical moments. Using various actuators and sensors, we introduce a new approach to detect vehicle dynamics instability and the turnover threshold.
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 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.
Biological immune system (BIS) is a special type of control system that has strong robustness and self-adaptability. This thesis report proposes an artificial immune system algorithm to develop an immune controller. The idea of immune controller is adopted and derived from biological vertebrate immune system, mimicking and imitating of biological immune system which is better known as the artificial immune system. This book show how proposes to apply and implement the algorithm of the artificial immune system (AIS) to develop an immune controller (IC) for three tank level control. There are various models of artificial immune controller (AIC). The most suitable for their particular application is selected. The selected artificial immune controller has the resemblance of a PID controller. The immune controller enhances the performance and stability of the system.
In the field of power electronics and electric drives there have been tremendous improvements for the last three decades to achieve speed control of AC drives. The implementation of artificial intelligence controllers like Fuzzy logic, neural networks have improved the performance of AC drives. Among various speed control strategies of AC drives, Direct Torque Control(DTC) is one of the emerging technique. The torque ripple in DTC based AC drives (Induction motor/Synchronous motor) can be minimized by applying Fuzzy logic and Neural network controllers.
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.