Intelligent Feedback Control for Flexible Manufacturing Systems
Gross, James Raymond
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Permalink
https://hdl.handle.net/2142/70470
Description
Title
Intelligent Feedback Control for Flexible Manufacturing Systems
Author(s)
Gross, James Raymond
Issue Date
1987
Department of Study
Business Administration
Discipline
Business Administration
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Industrial
Artificial Intelligence
Abstract
To many, the Flexible Manufacturing System (FMS) is a key element in addressing the urgent need to rebuild and streamline the current industrial base. Combining flexibility with efficient production, these systems provide industry with the needed economic benefits to produce with high quality and low cost. Numbering today only several dozen, hundreds of such systems are anticipated prior to the year 2000.
Yet, these advanced automation systems are not without problems. No unified "theory" of scheduling has yet been described allowing the designers or operators of such systems to have confidence in the completeness or robustness of the computer-implemented rules of operation controlling the system's behavior. Despite all attempts, the operating rules of such systems rely on heuristics derived either from intuition or from past experience.
The purpose of this work is to integrate previous and emerging concepts in the areas of FMS design, modelling, "knowledge-based" systems, and control theory in order to develop a framework for the improved operation of Flexible Manufacturing Systems. A major problem which this proposed control system architecture will alleviate concerns the difficulties which arise during system operation resulting from a changing operating environment. The proposed system, termed herein the Intelligent Feedback Methodology (IFM), incorporates within the overall FMS control system the ability to "learn" in the sense of adaptive behavior based on previous experience and the "foresight" to evaluate (and sometimes avoid) potential future outcomes.
The IFM features four distinct components whose purpose are: (1) to forecast the imminent demands to be placed upon the system, (2) to nominate possible policy alternatives to be applied in controlling system behavior, (3) to assess the potential consequences of each nominated policy, and (4) to select policies for (short-term) use by the operating FMS.
Following the description of the IFM methodological structure, an experimental IFM implementation is described and results discussed. The relative influences of selected factors upon the methodology's effectiveness within the experimental context are also addressed. Relationships between the IFM and other current research areas (i.e. Artificial Intelligence and Decision Support Systems) are also identified as well as avenues for additional investigation.
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