Main description:

The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. In the mid-1960s and contemporary with Kalman’s pioneering papers on sta- space models and optimal control, L.A. Zadeh began publishing papers on “fuzzy sets”. It took another decade before the fuzzy-logic controller due to Mamdani and Assilion was reported in the literature (ca. 1974), and now the fuzzy-logic control paradigm is entering its fifth decade of development and application. Thus, this new Advances in Industrial Control monograph edited by Ying Bai, Hanqi Zhuang and Dali Wang on fuzzy-logic control and its practical application comes as a timely reminder of the wide range of problems that can be solved by this continually evolving methodology.

Feature:

Describes the real-world uses of new fuzzy techniques to simplify readers’ tuning processes and enhance the performance of their control systems

Application examples drawn from a spectrum of advanced practical implementations cater to the current needs of engineers working with intelligent control

Specialist authors supply information for each application

Back cover:

The ability of fuzzy systems to provide shades of gray between "on or off" and "yes or no" is ideally suited to many of today’s complex industrial control systems. The static fuzzy systems usually discussed in this context fail to take account of inputs outside a pre-set range and their off-line nature makes tuning complicated.

Advanced Fuzzy Logic Technologies in Industrial Applications addresses the problem by introducing a dynamic, on-line fuzzy inference system. In this system membership functions and control rules are not determined until the system is applied and each output of its lookup table is calculated based on current inputs.

The tuning process is a major focus in this volume because it is the most difficult stage in fuzzy control application. Using new methods such as µ-law technique, histogram equalization and the Bezier-based method, all detailed here, the tuning process can be significantly simplified and control performance improved.

The other great strength of this book lies in the range and contemporaneity of its applications and examples which include: laser tracking and control; robot calibration; image processing and pattern recognition; medical engineering; audio systems; autonomous underwater vehicles and data mining.

Advanced Fuzzy Logic Technologies in Industrial Applications is written to be easily understood by readers not having specialized knowledge of fuzzy logic and intelligent control. Design and application engineers and project managers working in control, as well as researchers and graduate students in the discipline will find much to interest them in this work.

Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Contents:

Conventional, Intelligent and Fuzzy Logic Control.- Fundamentals of Fuzzy Logic Control.- Static, Dynamic and Real-time Fuzzy Logic Control and Implementation.- Knowledge-based Tuning I: µ-Law Tuning of a Fuzzy Lookup Table.- Knowledge-based Tuning II: Design and Tuning of Fuzzy Control Surfaces with Bezier Functions.- Fuzzy Logic Control Applied in a Laser Tracking System.- Fuzzy Logic for Robot Calibrations.- Fuzzy Logic for Image Processing and Pattern Recognition.- Fuzzy Logic For Medical Engineering.- Fuzzy Logic for Transportation Guidance.- Fuzzy Logic Control for Automobiles I: Knowledge-base Gear-position Decision for Automatic Vehicles.- Fuzzy Logic Control for Automobiles II: Car Navigation and Collision Avoidance System with Fuzzy Logic.- Fuzzy Logic for Autonomous Mobile Robots.- Fuzzy Logic Control for Autonomous Underwater Vehicles.- Fuzzy Logic for Flight Control.- Fuzzy Logic for Audio Systems.- Fuzzy Logic in Data Mining.- Fuzzy Logic Control for Power Networks.- Fuzzy Logic for Servo Control Systems.- Fuzzy Control of Manufacturing Welding Systems.- Fuzzy Predictive Control of a Solar Power Plant.