Neural and fuzzy logic control of drives and power systems pdf

With these tools, a system is said to be intelligent, learning, or have selforganizing capability. This chapter provides an overview on the elements of neural control. Fuzzy neural systems witnessed many progresses that made them applicable more efficiently to many applications such as automatic control. He presented it not as a control methodology, but as a way of processing data by allowing partial set membership rather than crisp set membership or nonmembership. It is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence ai. Sc is generally made up of fuzzy logic systems fls, the artificial neural networks ann the genetic algorithms ga and the hybrid systems, in particular the neuro fuzzy fuzzy neural, and the genetic assisted neural and fuzzy systems. Neural networks are capable of approximating any multidimensional nonlinear functions andas suchthey canbe very useful in nonlinear control 12. Power electronics converter circuits possess inherent characteristics, such as nonlinearities, unavailability of a precise model or its excessive complexity, that make them well suited for fuzzy logic control. Neuralnetwork and fuzzylogic learning and control of linear. Every effort was made to ensure the material was selfcontained and requires no specific experience in fuzzy logic methods. Fuzzy logic control and artificial neural network can be over come by the use of adaptive neuro fuzzy inference system the main concept of a neuro fuzzy network is derived from the human learning process, where an initial knowledge of a function is first setup by fuzzy rules and then the degree. This paper presents a methodology for implementation of a rulebased fuzzy logic controller applied to a closed loop voltshz speed control of induction motor. A servo motor drives based on hybrid genetic algorithm fuzzy. Dasl follows this basic idea but fully supports full.

The designed fuzzy logic controllers performance is weighed is compared with that of a pi controller. The ai techniques are generally classified as expert systems es, fuzzy logic fl and artificial neural networks ann. Generally, the proposed controller accepts both the output of the plant yp and the. Other special notes of interest, including an errata sheet if necessary. Free university, collage, school and other all books in pdf form. Neural and fuzzy logic control of drives and power systems by m n cirstea, andrei dinu, jeen g khor and malcolm mccormick topics. Subsequently, examples of a system for stabilization control is presented. Recently, fuzzy logic applications to the control of power electronics and drives are on rise. Over the past 25 years or so, feasibility of the application of ai for a variety of topics in power systems has been explored by a number of investigators.

A recent hardware design and implementation of fuzzy logic controllers builds all fuzzy control modules on a fieldprogrammable gate array fpga chipset. Neural and fuzzy logic control of drives and power systems core. Neural and fuzzy logic control of drives and power systems free epub, mobi, pdf ebooks download, ebook torrents download. Speed control of dc motor using fuzzy pid controller. Purchase neural and fuzzy logic control of drives and power systems 1st edition. Adaptive neural fuzzy inference system anfis is a hybrid between artificial neural networks ann and fuzzy logic control flc that enhances the execution of direct torque controlled drives and. Introduces cuttingedge control systems to a wide readership of engineers and studentsthe first book on neuro fuzzy control systems to take a practical, applicationsbased approach, backed up with worked examples and case studieslearn to use vhdl in realworld applicationsintroducing cutting edge control systems through realworld applicationsneural networks and fuzzy logic based systems.

However, developments of neural or fuzzy systemsarenottrivial. Fuzzy logic in embedded microcomputers and control systems. Free engineering books and other many books so neural and fuzzy logic control of drives and power systems by m. Pdf neural and fuzzy logic control of drives and power. Implementation of adaptive neuro fuzzy inference system in. Generally known as adaptive control systems, they include techniques such as selftuning control, hinfinity control, model referencing adaptive control and sliding mode. Tutorial on fuzzy logic applications in power systems. In the linear case, one has to mention the scalar control which is the first scheme proposed for this task. Whether youve loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them. Its not as fuzzy as you might think and has been working quietly behind the scenes for years. Other readers will always be interested in your opinion of the books youve read. The anfis is, from the topology point of view, an implementation of a representative fuzzy inference system using a back propagation neural network structure. A fuzzy control system is a control system based on fuzzy logica mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 true or false, respectively. 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.

To overcome the drawbacks of neural networks and fuzzy logic, adaptive neuro fuzzy inference system anfis was proposed in this paper. The induction motor is modelled using a dq axis theory. Oct 05, 20 neural and fuzzy logic control of drives and power systems, andrei dinu download here. The authors guide readers quickly and concisely through the complex topics of neural networks, fuzzy logic. Neural and fuzzy logic control of drives and power systems cirstea, marcian, dinu, andrei, mccormick, malcolm, khor, jeen ghee on. Table 3 is a list of the more common application areas. Neural and fuzzy logic control of drives and power systems by.

Adaptive neural fuzzy inference systems controller for hybrid. Fuzzy logic and neural networks in power electronics and. This is followed by a general section on fuzzy logic techniques and methods. Dnns with logic focused on propositional logic, which supports only logical connectives between atomic propositions garcez et al. Abstract in this paper we have designed a dc motor whose speed can be controlled using pid controller. Speed control of separately excited dc motor using fuzzy. Fuzzy logic is a rulebased system that can rely on the practical experience of an operator, particularly useful to capture experienced operator knowledge. Pdf expert system, fuzzy logic, and neural network. Neural and fuzzy logic control of drives and power systems. The authors guide readers quickly and concisely through the complex topics of. Fuzzy logic controller an overview sciencedirect topics.

Applications of fuzzy logic fl to power electronics and drives are on the rise. Component of getting a successful web marketing campaign is tracking and analyzing results every day. An immediate synergy can be found between fuzzy and neural control. Pdf fuzzy logic application to power electronics and drives. Speed control of dc motor using fuzzy pid controller 1umesh kumar bansal and 2rakesh narvey 1dept. Unlike the academic monographs that have previously b. Thus, it is hoped that practitioners at all levels will.

Neural and fuzzy logic control of drives and power systems, andrei dinu download here. Generally, we use response circuit to generate an intelligent response which intakes signal data from we describe, in this book, recent advances on the design of intelligent systems based on fuzzy logic, neural networks, and natureinspired optimization and their application in areas, such as intelligent control and robotics, pattern. Marcian cirstea andrei dinu malcolm mccormick jeen. Linear neural networks applied to power converters and ac. Introduction fuzzy logic has rapidly become one of the most successful of todays technologies for developing sophisticated control systems. The general layout of the controller chip in a unity feedback control system is shown in figure 7. As part of the last topic, two new flexible and expeditious training strategies are developed to train the neural network and fuzzy logic controllers for both naval and civil engineering applications. Fuzzy logic and neural network applications in ac drives. Pdf fuzzy logic application to power electronics and. In the context of power systems, application of artificial neural networks anns and fuzzy logic is commonly referred to in the literature as ai applications in power systems. Various versi ons of c and matlab code for simulation of fuzzy controllers, fuzzy control systems, adaptive fuzzy identi. Expert system, fuzzy logic, and neural network applications.

Marcian cirstea andrei dinu malcolm mccormick jeen ghee khor neural and fuzzy logic control of drives and power systems 2002 newnes. Traditionally, the design of a control system is dependent on the explicit description. At the same time, this booklet includes contributions, which are undoubtedly state of theart research. Artificial intelligence ai tools, such as expert systems, fuzzy logic, and neural networks are expected to usher a new era in power electronics and motion control in the coming decades. Artificial intelligence applications in power systems. To impart the knowledge of neural networks and fuzzy logic control and use these for controlling real time systems. Aspects of fuzzy logic and neural nets world scientific series in robotics and automated systems report. The concept of fuzzy logic fl was conceived by lotfi zadeh, a professor at the university of california at berkley in 1965. Certificate this thesis on speed control of separately excited dc motor using fuzzy control logic is presented in completion of partial requirement of bachelors degree in electrical engineering is written. Design and simulation of fuzzy logic controller for power.