Intelligent Control Principles Techniques And Applications

Introducton; Methology of knowledge representation; General inference principles; Hierarchical control systems; Expert control systems; Fuzzy control systems; Neurocontrol systems; Learning control systems; Intelligente control systems in application; Prospectives of intelligente control; References; Bibliography; Subject index.

This book is concerned with Intelligent Control methods and applications. The field of intelligent control has been expanded very much during the recent years and a solid body of theoretical and practical results are now available. These results have been obtained through the synergetic fusion of concepts and techniques from a variety of fields such as automatic control, systems science, computer science, neurophysiology and operational research. Intelligent control systems have to perform anthropomorphic tasks fully autonomously or interactively with the human under known or unknown and uncertain environmental conditions. Therefore the basic components of any intelligent control system include cognition, perception, learning, sensing, planning, numeric and symbolic processing, fault detection/repair, reaction, and control action. These components must be linked in a systematic, synergetic and efficient way. Predecessors of intelligent control are adaptive control, self-organizing control, and learning control which are well documented in the literature. Typical application examples of intelligent controls are intelligent robotic systems, intelligent manufacturing systems, intelligent medical systems, and intelligent space teleoperators. Intelligent controllers must employ both quantitative and qualitative information and must be able to cope with severe temporal and spatial variations, in addition to the fundamental task of achieving the desired transient and steady-state performance. Of course the level of intelligence required in each particular application is a matter of discussion between the designers and users. The current literature on intelligent control is increasing, but the information is still available in a sparse and disorganized way.

This book introduces the development process, structural theories and research areas of intelligent control; explains the knowledge representations, searching and reasoning mechanisms as the fundamental techniques of intelligent control; studies the theoretical principles and architectures of various intelligent control systems; analyzes the paradigms of representative applications of intelligent control; and discusses the research and development trends of the intelligent control.From the general point of view, this book possesses the following features: updated research results both in theory and application that reflect the latest advances in intelligent control; closed connection between theory and practice that enables readers to use the principles to their case studies and practical projects; and comprehensive materials that helps readers in understanding and learning.

The emergence of fuzzy logic and its applications has dramatically changed the face of industrial control engineering. Over the last two decades, fuzzy logic has allowed control engineers to meet and overcome the challenges of developing effective controllers for increasingly complex systems with poorly defined dynamics. Today's engineers need a working knowledge of the principles and techniques of fuzzy logic-Intelligent Control provides it. The author first introduces the traditional control techniques and contrasts them with intelligent control. He then presents several methods of representing and processing knowledge and introduces fuzzy logic as one such method. He highlights the advantages of fuzzy logic over other techniques, indicates its limitations, and describes in detail a hierarchical control structure appropriate for use in intelligent control systems. He introduces a variety of applications, most in the areas of robotics and mechatronics but with others including air conditioning and process/production control. One appendix provides discussion of some advanced analytical concepts of fuzzy logic, another describes a commercially available software system for developing fuzzy logic application. Intelligent Control is filled with worked examples, exercises, problems, and references. No prior knowledge of the subject nor advanced mathematics are needed to comprehend much of the book, making it well-suited as a senior undergraduate or first-year graduate text and a convenient reference tool for practicing professionals.

Intelligent Systems and Control: Principles and Applications is a textbook for undergraduate level courses on intelligent control, intelligent systems, adaptive control, and non-linear control. The book covers primers in neural networks, fuzzy logic, and non-linear control so that readers can easily follow intelligent control techniques.

This book reflects the work of top scientists in the field of intelligent control and its applications, prognostics, diagnostics, condition based maintenance and unmanned systems. It includes results, and presents how theory is applied to solve real problems.

Intelligent control is a rapidly developing, complex and challenging field with great practical importance and potential. Because of the rapidly developing and interdisciplinary nature of the subject, there are only a few edited volumes consisting of research papers on intelligent control systems but little is known and published about the fundamentals and the general know-how in designing, implementing and operating intelligent control systems. Intelligent control system emerged from artificial intelligence and computer controlled systems as an interdisciplinary field. Therefore the book summarizes the fundamentals of knowledge representation, reasoning, expert systems and real-time control systems and then discusses the design, implementation verification and operation of real-time expert systems using G2 as an example. Special tools and techniques applied in intelligent control are also described including qualitative modelling, Petri nets and fuzzy controllers. The material is illlustrated with simple examples taken from the field of intelligent process control.

Intelligent Control techniques are becoming important tools in both academia and industry. Methodologies developed in the field of soft-computing, such as neural networks, fuzzy systems and evolutionary computation, can lead to accommodation of more complex processes, improved performance and considerable time savings and cost reductions. Intelligent Control Systems using Computational Intellingence Techniques details the application of these tools to the field of control systems. Each chapter gives and overview of current approaches in the topic covered, with a set of the most important references in the field, and then details the author's approach, examining both the theory and practical applications.