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.
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.
This book explains the principles of different types of Neural Networks such as Feed Forward, Cascade Feed Forward and Radial Basis Function Neural Networks. It also describes Fuzzy Logic concepts and Membership Functions. It is needed to mention that Neuron-Fuzzy Inference systems are come from Fuzzy Logic and Neural Network concepts; these are adaptive techniques that are given in detail in this book. Support Vector Machines are presented here as well. Applications such as direct current motors, student administration system, and electrical faults are employed to implement the above soft computing techniques.
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.
This book considers the problem of oscillations in synchronous generator connected to infinite bus through transmission lines. Two control techniques namely; artificial neural networks and fuzzy logic will be used to cancel the oscillations in the synchronous generators. Simulation results of applying external disturbances to the synchronous generator controlled by the proposed fuzzy logic controllers are compared to results obtained by using neural network controllers. These controllers based on excitation control are used to improve the performance of the synchronous generator. The simulation results show the advantages and the improvements with using the proposed fuzzy logic controllers over using the proposed neural network controllers. The potentials of the proposed techniques are investigated in time and frequency domains by using MATLAB software package.
The combination of Artificial Neural Network and Fuzzy Logic are probably the most attractive techniques among the researchers in recent times which is capable of handling non-linear, imprecise, fuzzy, noisy and probabilistic information to solve complex problem in efficient manner. Hybrid systems are designed to take advantage of the strengths of each system and avoid the limitations of each system. It is natural for neural networks to learn but it is cumbersome for a fuzzy system to learn. Hence a combination of the two would result in a rule- based system that can learn and adapt. This book, therefore, provide a comprehensive and integrated approach using Fuzzy logic and Artificial neural network techniques in modeling selected hydrological problems related to International river Brahmaputra within India. Four different hydrological problems are modeled using the proposed fuzzy – neural network approach for examining the usefulness of it. This comprehensive real time hydrological modelling study should be especially useful to the Hydrologist, civil engineers, agriculturists, students, field engineers and related governmental as well as non-governmental organisations.
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.
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 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 work details with the design and development of fuzzy modelling for multi variable process in distillation column. Salient Features •Fuzzy modelling of distillation column using Takagi – Sugeno model. •Design a fuzzy model predictive control (FMPC) for multi variable control of distillation column •Optimization of the system with different optimization algorithms •Comparison of the results of distillation column controlled with conventional PI controller, Model Predictive Control and FMPC. •Development of neural network model for distillation column •Design of Adaptive Neuro Fuzzy Interference System (ANFIS) for distillation column and to control the parameters. •Comparison of the results of this system controlled with conventional PI controller, Model Predictive Control and ANFIS
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.
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.
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.