TY - JOUR
T1 - A structured adaptive supervisory control methodology for modeling the control of a discrete event manufacturing system
AU - Qiu, Robin G.
AU - Joshi, Sanjay B.
N1 - Funding Information:
Manuscript received April 15, 1996; revised September 16, 1996; May 12, 1999; and July 20, 1999. This work was supported in part by NSF Presidential Young Investigator Award DDM9158042. This paper was recommended by Associate Editor C. Hsu. R. G. Qiu is with the Factory Systems Division, Kulicke & Soffa Industries, Inc., Willow Grove, PA 19090 USA (e-mail: rqiu@eng.kns.com). S. B. Joshi is with the Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA 16802 USA. Publisher Item Identifier S 1083-4427(99)08394-0.
PY - 1999
Y1 - 1999
N2 - Two basic measures, model complexity and model construction efficiency, are usually used to evaluate the implementability (or ease of use in practice) of a methodology for modeling the control of a discrete event manufacturing system (DEMS) on the shop floor. Many well-recognized methods are used to represent and analyze the dynamics of DEMS's, but not many relevant applications have been found in developing control software for the shop floor due to their shortcomings in satisfying these two measures. This paper explores a methodology for modeling the control of a DEMS, which leads to ease of control software development, rather than a new representational/analytical tool, by significantly reducing the model complexity (in terms of the number of required control states) and improving the model construction efficiency. First, an extended finite machine, called a deterministic finite capacity machine (DFCM) with parallel computing capability is developed. Based on DFCM's, the complexity growth function of a DEMS control model is linear in the number of synthesized control components. Then, an automaton structure of a DFCM control model, called structured adaptive supervisory control (SASC), is developed. By referring to supervisory control theory, an SASC model is created with three function layers: acceptance, adaptive supervision, and execution. The well-defined structure ensures that the control model can be constructed systematically.
AB - Two basic measures, model complexity and model construction efficiency, are usually used to evaluate the implementability (or ease of use in practice) of a methodology for modeling the control of a discrete event manufacturing system (DEMS) on the shop floor. Many well-recognized methods are used to represent and analyze the dynamics of DEMS's, but not many relevant applications have been found in developing control software for the shop floor due to their shortcomings in satisfying these two measures. This paper explores a methodology for modeling the control of a DEMS, which leads to ease of control software development, rather than a new representational/analytical tool, by significantly reducing the model complexity (in terms of the number of required control states) and improving the model construction efficiency. First, an extended finite machine, called a deterministic finite capacity machine (DFCM) with parallel computing capability is developed. Based on DFCM's, the complexity growth function of a DEMS control model is linear in the number of synthesized control components. Then, an automaton structure of a DFCM control model, called structured adaptive supervisory control (SASC), is developed. By referring to supervisory control theory, an SASC model is created with three function layers: acceptance, adaptive supervision, and execution. The well-defined structure ensures that the control model can be constructed systematically.
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U2 - 10.1109/3468.798061
DO - 10.1109/3468.798061
M3 - Article
AN - SCOPUS:0011798295
VL - 29
SP - 573
EP - 586
JO - IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
JF - IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
SN - 1083-4427
IS - 6
ER -