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 extendedexposition of new work in all aspects of industrial control.

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 Includes supplementary material: sn.pub/extras

Autorentext

Doctor Ying Bai has been working in the field of fuzzy logic control since 1995. He has published three textbooks and about 20 research papers in international conferences and journals, and most of them are related to the fuzzy logic control on DC/AC motors, laser tracking systems and modeless robots calibrations. He is currently teaching at the Department of Computer Science and Engineering at Johnson C. Smith University.

Dr. Zhuang is an Associate Editor of IEEE Transactions on Robotics and International Journal of Computer Applications . He has received a number of awards and grants, including a NSF Young Investigator Award. He has published one research monograph and 50 refereed journal papers on the subjects of robotics, computer vision and fuzzy logic control. His recent research activities include conducting a project with DOD/DISA on secure telecommunication using fuzzy logic and biometrics.

Dr. Dali Wang is an Assistant Professor at Christopher Newport University. He has over 20 refereed research papers in the areas of digital signal processing, soft computing and robotics. Since 1995, he has been extensively involved in work on the applications of soft computing techniques, including neural networks and fuzzy logic, in many industrial areas: digital signal processing, telecommunications, control and robotics. He gained practical perspective from his five years' industrial experience in the semiconductor, wireless and network communication industry. Much of his research work is involved in combining theoretical aspects and practical implementation.

Inhalt
From Classical Control to Fuzzy Logic Control.- Fundamentals of Fuzzy Logic Control Fuzzy Sets, Fuzzy Rules and Defuzzifications.- Implementation of Fuzzy Logic Control Systems.- Knowledge-based Tuning I: Design and Tuning of Fuzzy Control Surfaces with Bezier Function.- Knowledge-based Tuning II: ?-Law Tuning of a Fuzzy Lookup Table.- Apply a Fuzzy Logic Controller to Suppress Noises and Coupling Effects for a Laser Tracking System.- Fuzzy Logic for Image Processing: Definition and Applications of a Fuzzy Image Processing Scheme.- Fuzzy Logic for Medical Engineering: An Application to Vessel Segmentation.- Fuzzy Logic for Transportation Guidance: Developing Fuzzy Controllers for Maintaining an Inter-Vehicle Safety Headway.- Fuzzy Logic Control for Automobiles I: Knowledge-based Gear-position Decision.- Fuzzy Logic Control for Automobiles II: Navigation and Collision Avoidance System.- Fuzzy Logic Based Control Mechanisms for Handling the Uncertainties Facing Mobile Robots in Changing Unstructured Environments.- Combine Sliding Mode Control and Fuzzy Logic Control for Autonomous Underwater Vehicles.- Fuzzy Logic for Flight Control I: Nonlinear Optimal Control of Helicopter Using Fuzzy Gain Scheduling.- Fuzzy Logic for Flight Control II: Fuzzy Logic Approach to Path Tracking and Obstacles Avoidance of UAVs.- Close Formation Flight Control of Multi-UAVs via Fuzzy Logic Technique.- Applications of Fuzzy Logic in Data Mining Process.- Fuzzy Logic Control for Power Networks: A Multilayer Fuzzy Controller.- Fuzzy Predictive Control for Power Plants.- Fuzzy Logic for Robots Calibration Using Fuzzy Interpolation Technique in Modeless Robot Calibration.- Fuzzy Control on Manufacturing Welding Systems: To Apply Fuzzy Theory in the Control of Weld Line of PlasticInjection-Molding.