Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

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A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

The notion of fuzziness as defined in this paper relates to situations in which the source of imprecision is not a random variable or a stochastic process, but rather a class or classes which do not possess sharply defined boundaries, e.g., the “class of bald men,” or the “class of numbers which are much greater than 10,” or the “class of adaptive systems,” etc. A basic concept which makes it possible to treat fuzziness in a quantitative manner is that of a fuzzy set, that is, a class in which there may be grades of membership intermediate between full membership and non-membership. Thus, a fuzzy set is characterized by a membership function which assigns to each object its grade of membership (a number lying between 0 and 1) in the fuzzy set. After a review of some of the relevant properties of fuzzy sets, the notions of a fuzzy system and a fuzzy class of systems are introduced and briefly analyzed. The paper closes with a section dealing with optimization under fuzzy constraints in which an approach to...

Fuzzy sets and systems

Innovation in concentrated solar power

This work focuses on innovation in CSP technologies over the last decade. A multitude of advancements has been developed during this period, as the topic of concentrated solar power is becoming more mainstream. Improvements have been made in reflector and collector design and materials, heat absorption and transport, power production and thermal storage. Many applications that can be integrated with CSP regimes to conserve (and sometimes produce) electricity have been suggested and implemented, keeping in mind the environmental benefits granted by limited fossil fuel usage.

Innovation in Concentrated Solar Power

For the past three decades, computer-aided process planning (CAPP) has attracted a large amount of research interest. A huge volume of literature has been published on this subject. Today, CAPP research faces new challenges owing to the dynamic markets and business globalisation. Thus, there is an urgent need to ascertain the current status and identify future trends of CAPP. Covering articles published on the subjects of CAPP in the past 10 years or so, this article aims to provide an up-to-date review of the CAPP research works, a critical analysis of journals that publish CAPP research works, and an understanding of the future direction in the field. First, general information is provided on CAPP. The past reviews are summarised. Discussions about the recent CAPP research are presented in a number of categories, i.e. feature-based technologies, knowledge-based systems, artificial neural networks, genetic algorithms, fuzzy set theory and fuzzy logic, Petri nets, agent-based technology, Internet-based technology, STEP-compliant CAPP and other emerging technologies. Research on some specific aspects of CAPP is also provided. Discussions and analysis of the methods are then presented based on the data gathered from the Elsevier's Scopus abstract and citation database. The concepts of 'Subject Strength' of a journal and 'technology impact factor' are introduced and used for discussions based on the publication data. The former is used to gauge the level of focus of a journal on a particular research subject/domain, whereas the latter is used to assess the level of impact of a particular technology, in terms of citation counts. Finally, a discussion on the future development is presented.

Computer-aided process planning-A critical review of recent developments and future trends

Since knowledge in an expert system is vague and modified frequently, expert systems are fuzzy and dynamic. It is very important to design a dynamic knowledge inference framework which is adjustable according to knowledge variation as human cognition and thinking. A generalized fuzzy Petri net model, called adaptive fuzzy Petri net (AFPN), is proposed with this object in mind. AFPN not only has the descriptive advantages of the fuzzy Petri net, it also has learning ability like a neural network. Just as other fuzzy Petri net (FPN) models, AFPN can be used for knowledge representation and reasoning, but AFPN has one important advantage: it is suitable for dynamic knowledge, i.e., the weights of AFPN are adjustable. Based on the AFPN transition firing rule, a modified backpropagation learning algorithm is developed to assure the convergence of the weights.

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Dynamic knowledge inference and learning under adaptive fuzzy Petri net framework

Knowledge in some fields like Medicine, Science and Engineering is very dynamic because of the continuous contributions of research and development. Therefore, it would be very useful to design knowledge-based systems capable to be adjusted like human cognition and thinking, according to knowledge dynamics. Aiming at this objective, a more generalized fuzzy Petri net model for expert systems is proposed, which is called AFPN (Adaptive Fuzzy Petri Nets). This model has both the features of a fuzzy Petri net and the learning ability of a neural network. Being trained, an AFPN model can be used for dynamic knowledge representation and inference. After the introduction of the AFPN model, the reasoning algorithm and the weight learning algorithm are developed. An example is included as an illustration.

Adaptive fuzzy petri nets for dynamic knowledge representation and inference

Model reference adaptive control (MRAC) is a popular approach to control linear systems, as it is relatively simple to implement. However, the performance of the linear MRAC deteriorates rapidly when the system becomes nonlinear. In this paper, a nonlinear MRAC based on neurofuzzy networks is derived. Neurofuzzy networks are chosen not only because they can approximate nonlinear functions with arbitrary accuracy, but also they are compact in their supports, and the weights of the network can be readily updated on-line. The implementation of the neurofuzzy network-based MRAC is discussed, and the local stability of the system controlled by the proposed controller is established. The performance of the neurofuzzy network-based MRAC is illustrated by examples involving both linear and nonlinear systems.

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Model-reference adaptive control based on neurofuzzy networks

Neurofuzzy network modelling and control of steam pressure in 300 MW steam-boiler system

Parabolic dish solar concentrators are very expensive devices with a cost of up to a half of the total cost of a solar power station. The specific technology of parabolic mirror manufacturing makes the decrease of the cost very problematic. Even in mass production the cost of a parabolic mirror is estimated as $500 per square meter. There is another way to make a parabolic concentrator by approximating a parabolic surface with large number of small flat mirrors. We created a small prototype of this type of solar concentrator which contains 24 flat mirrors in the form of equilateral triangles with side length of 50mm. This prototype has special nuts to adjust the positions of the nodes in the points of connections of the apexes. These nuts make it possible to approximate the parabolic shape in an easy and inexpensive way.

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Felipe Lara-Rosano

Papers

Dynamic knowledge inference and learning under adaptive fuzzy Petri net framework

Adaptive fuzzy petri nets for dynamic knowledge representation and inference

Model-reference adaptive control based on neurofuzzy networks

Neurofuzzy network modelling and control of steam pressure in 300 MW steam-boiler system

Flat Facet Parabolic Solar Concentrator With Support Cell for One and More Mirrors