This Proceedings contains the papers presented at the 9th IFAC AIRTC'2000 Symposium on Artificial Intelligence in Real-Time Control 2000, held at Budapest Polytechnic, Hungary, on 2 - 4 October. AIRTC'2000 builds on the excellent reputation of previous meetings in the series for providing top-quality papers in this important research field. A positive development illustrated by this Proceedings is a new trend towards pragmatism in the research field. Examples of this trend are: an increase in the number of actual industrial applications; support for more widespread use of new sophisticated technologies (e.g. materials design); further intertwining of artificial intelligence and control theory methods that reduces the reliance on blind faith, still too often associated with AI methods. Many things have changed since the first AIRTC event in 1988. Two examples illustrate the change in the general attitude of the IFAC family: in 1990, one of the major closing presentations of the IFAC World Congress warned the control community about the coming hordes of AI people. In 1999, one of the plenary papers at the IFAC World Congress pointed out that the AI based methods form a natural extension of control theory to the class of non-linear systems with incomplete information (at least as far as the optimisation is concerned). This contrast in attitudes shows how, during the past decade, many AI people have embraced control theory and many control people have learned the basics of AI. This Proceedings serves to continue this excellent dialogue, by providing many quality papers which link both fields.

Paperback. Artificial Intelligence is one of the new technologies that has contributed to the successful development and implementation of powerful and friendly control systems. These systems are more attractive to end-users shortening the gap between control theory applications. The IFAC Symposia on Artificial Intelligence in Real Time Control provides the forum to exchange ideas and results among the leading researchers and practitioners in the field. This publication brings together the papers presented at the latest in the series and provides a key evaluation of present and future developments of Artificial Intelligence in Real Time Control system technologies.

Paperback. The Symposium on Artificial Intelligence in Real-Time Control 97 (AIRTC '97) was the seventh in the series of symposia and workshops under the sponsorship of the International Federation of Automatic Control's (IFAC) Co-ordinating Committee in Computer Control and of the Technical Committee on Artificial Intelligence in Real-Time Control.Artificial Intelligence methods, including expert systems, artificial neural networks, fuzzy systems and genetic algorithms, are penetrating almost every field of engineering. These methods have shown their possible application in control, monitoring and supervising tasks which are difficult or impossible to solve when using conventional techniques. We have now come to a stage where there is a need to discuss and present these methods in a broader framework, not only showing their concepts and available algorithms, but also their relative benefits, advantages and disadvantages. This was the purpose of th

This book brings together the contributions of leading researchers inthe field of machine intelligence, covering areas such as fuzzy logic, neural networks, evolutionary computation and hybrid systems.There is wide coverage of the subject from simple tools, throughindustrial applications, to applications in high-level intelligentsystems which are biologically motivated, such as humanoid robots (andselected parts of these systems, like the visual cortex). Readers willgain a comprehensive overview of the issues in machine intelligence, afield which promises to play a very important role in the informationsociety of the future

The book “Soft Computing Based Modeling in Intelligent Systems”contains the - tended works originally presented at the IEEE International Workshop SOFA 2005 and additional papers. SOFA, an acronym for SOFt computing and Applications, is an international wo- shop intended to advance the theory and applications of intelligent systems and soft computing. Lotfi Zadeh, the inventor of fuzzy logic, has suggested the term “Soft Computing.” He created the Berkeley Initiative of Soft Computing (BISC) to connect researchers working in these new areas of AI. Professor Zadeh participated actively in our wo- shop. Soft Computing techniques are tolerant to imprecision, uncertainty and partial truth. Due to the large variety and complexity of the domain, the constituting methods of Soft Computing are not competing for a comprehensive ultimate solution. Instead they are complementing each other, for dedicated solutions adapted to each specific pr- lem. Hundreds of concrete applications are already available in many domains. Model based approaches offer a very challenging way to integrate a priori knowledge into procedures. Due to their flexibility, robustness, and easy interpretability, the soft c- puting applications will continue to have an exceptional role in our technologies. The applications of Soft Computing techniques in emerging research areas show its mat- ity and usefulness. The IEEE International Workshop SOFA 2005 held Szeged-Hungary and Arad- Romania in 2005 has led to the publication of these two edited volumes. This volume contains Soft Computing methods and applications in modeling, optimisation and prediction.

This book provides an overview of the noteworthy developments in the field of micromachining, with a specific focus on microinjection systems used for biological micromanipulation. The author also explores the design, development, and fabrication of new mechanical designs for micromachines, with plenty of examples that elucidate their modeling and control. The design and fabrication of a piezoelectric microinjector, constant force microinjector, constant force microgripper, PDVF microforce sensor, and a piezoelectric microsyringe are presented as examples of new technology for microinjection systems. This book is appropriate for both researchers and advanced students in bioengineering.

This book provides engineers and scientists in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. It equips them to apply the models and methods discussed to real problems with confidence, while also making them aware of potential difficulties that may arise in practice. Moreover, the book is self-contained, requiring only a basic grasp of matrix algebra, signals and systems, and statistics. Accordingly, it can also serve as an introduction to linear system identification, and provides a practical overview of the major optimization methods used in engineering. The focus is on gaining an intuitive understanding of the subject and the practical application of the techniques discussed. The book is not written in a theorem/proof style; instead, the mathematics is kept to a minimum, and the ideas covered are illustrated with numerous figures, examples, and real-world applications. In the past, nonlinear system identification was a field characterized by a variety of ad-hoc approaches, each applicable only to a very limited class of systems. With the advent of neural networks, fuzzy models, Gaussian process models, and modern structure optimization techniques, a much broader class of systems can now be handled. Although one major aspect of nonlinear systems is that virtually every one is unique, tools have since been developed that allow each approach to be applied to a wide variety of systems.

Improvements in process control, such as defined-accuracy instrumentation structures and computationally intelligent process modeling, enable advanced capabilities such as molecular manufacturing. High Performance Instrumentation and Automation demonstrates how systematizing the design of instrumentation and automation leads to higher performance through more homogeneous systems, which are frequently assisted by rule-based, fuzzy logic, and neural network process descriptions. Incorporate Advanced Performance Enhancements into Your Automation Enterprise The book illustrates generic common core process-to-control concurrent engineering linkages applied to a variety of laboratory and industry automation systems. It outlines: Product properties translated into realizable process variables Axiomatic decoupling of subprocess variables for improved robustness Production planner model-driven goal state execution In situ sensor and control structures for attenuating process disorder Apparatus tolerance design for minimizing process variabilities Production planner remodeling based on product features measurement for quality advancement Coverage also includes multisensor data fusion, high-performance computer I/O design guided by comprehensive error modeling, multiple sensor algorithmic error propagation, robotic axes volumetric accuracy, quantitative video digitization and reconstruction evaluation, and in situ process measurement methods. High Performance Instrumentation and Automation reflects the experience of engineer and author Patrick Garrett, including his role as co-principal investigator for an Air Force intelligent manufacturing initiative. You can download Analysis Suite.xls,, computer-aided design instrumentation software, available in the book's description on the CRC Press website.

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.

Robust control mechanisms customarily require knowledge of the system’s describing equations which may be of the high order differential type. In order to produce these equations, mathematical models can often be derived and correlated with measured dynamic behavior. There are two flaws in this approach one is the level of inexactness introduced by linearizations and the other when no model is apparent. Several years ago a new genre of control systems came to light that are much less dependent on differential models such as fuzzy logic and genetic algorithms. Both of these soft computing solutions require quite considerable a priori system knowledge to create a control scheme and sometimes complicated training program before they can be implemented in a real world dynamic system. Michie and Chambers’ BOXES methodology created a black box system that was designed to control a mechanically unstable system with very little a priori system knowledge, linearization or approximation. All the method needed was some notion of maximum and minimum values for the state variables and a set of boundaries that divided each variable into an integer state number. The BOXES Methodology applies the method to a variety of systems including continuous and chaotic dynamic systems, and discusses how it may be possible to create a generic control method that is self organizing and adaptive that learns with the assistance of near neighbouring states. The BOXES Methodology introduces students at the undergraduate and master’s level to black box dynamic system control , and gives lecturers access to background materials that can be used in their courses in support of student research and classroom presentations in novel control systems and real-time applications of artificial intelligence. Designers are provided with a novel method of optimization and controller design when the equations of a system are difficult or unknown. Researchers interested in artificial intelligence (AI) research and models of the brain and practitioners from other areas of biology and technology are given an insight into how AI software can be written and adapted to operate in real-time.