97 resultados para Neural Control Systems


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in the last 10 years many designs and trial implementations of holonic manufacturing systems have been reported in the literature. Few of these have resulted in any industrial take up of the approach and part of this lack of adoption might be attributed to a shortage of evaluations of the resulting designs and implementations and their comparison with more conventional approaches. This paper proposes a simple approach for evaluating the effectiveness of a holonic system design, with particular focus on the ability of the system to support reconfiguration (in the face of change). A case study relating to a laboratory assembly system is provided to demonstrate the evaluation approach. Copyright © 2005 IFAC.

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This paper will provide a rationale for developing control systems based on the availability of automated identification (Auto ID) information provision. Much of the Auto-ID research has to date focussed on developing the essential infrastructure for dynamically extracting, networking and storing product data. These developments will help to revolutionise the accuracy, quality and timeliness of data acquired by Business Information Systems and should lead to major cost savings and performance improvements as a result. This paper introduces an additional phase of Auto ID research and development in which the nature of control system decisions is reconsidered in the light of the availability of ubiquitous, unique, item-level information. The paper will: (i) Indicate why the availability of ubiquitous, unique, item-level data can enable enhanced and fundamentally different control approaches and highlight potential benefits from control systems incorporating this Auto ID data (ii) Demonstrate what is required to develop control systems based around the availability of Auto ID data. (iii) Outline the research challenges in determining how such systems will be developed.

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While a large amount of research over the past two decades has focused on discrete abstractions of infinite-state dynamical systems, many structural and algorithmic details of these abstractions remain unknown. To clarify the computational resources needed to perform discrete abstractions, this paper examines the algorithmic properties of an existing method for deriving finite-state systems that are bisimilar to linear discrete-time control systems. We explicitly find the structure of the finite-state system, show that it can be enormous compared to the original linear system, and give conditions to guarantee that the finite-state system is reasonably sized and efficiently computable. Though constructing the finite-state system is generally impractical, we see that special cases could be amenable to satisfiability based verification techniques. ©2009 IEEE.

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Driven by the need for more responsive manufacturing processes and as a consequence of increasing complexity in products and production systems, this short paper introduces a number of developments in the area of modular, distributed manufacturing systems. Requirements for the development of such systems are addressed and, in particular, the relevance to current and future integrated control systems is examined. One of the key issues for integrated control systems in the future is the need to provide support for distributed decision-making in addition to existing distributed control capabilities.

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The aim of this paper is to describe the implementation of a new approach for the introduction of so called 'holonic manufacturing' principles into existing production control systems. Such an approach is intended to improve the reconfigurability of the control system to cope with the increasing requirements of production change. A conceptual architecture is described and implemented in a robot assembly cell to demonstrate that this approach can lead to a manufacturing control system which can adapt relatively simply to long-term change. A design methodology and migration strategy for achieving these solutions using conventional hardware is proposed to develop execution level of manufacturing control systems.