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Automazione e Strumentazione

Novembre/Dicembre 2014

CONTROLLO

tecnica

83

lo di simulazione Des così ottenuto è sta-

to validato comparando i risultati delle si-

mulazioni con i dati reali acquisiti dall’im-

pianto pilota.

Eventuali sviluppi futuri possono essere

concentrati su obiettivi differenti come lo

sviluppo di un HLCS a struttura decen-

tralizzata basato sulla rappresentazione a

grafo orientato nonché sull’implementa-

zione del LLCS facente uso di altre tecni-

che di controllo.

Bibliografia

[1]

M. Bauer, I. K. Craig, “Economic

assessment of advanced process control –

a survey and framework”,

Journal of Pro-

cess Control

, Vol. 18, pp. 2-18, 2008.

[2]

J. Maciejowski,

Predictive control

with constraints

, Prentice Hall, 2002.

[3]

J.A. Rossiter,

Model based predictive

control: a practical approach

, CRC Press,

2003.

[4]

J.B. Rawlings, D.Q. Mayne,

Model

Predictive Control: Theory and Design

,

Nob Hill Publishing, 2009.

[5]

A. Cataldo, A. Perizzato, R. Scatto-

lini, “Management of a production cell

lubrication system with model predictive

control”,

Proc. APMS international con-

ference on Advances in production sys-

tems

, APMS 2014, Ajaccio, France, 20-24

September 2014, Part III, IFIP AICT 440,

pp. 131-138, 2014.

[6]

A. N. Tarau, B. De Schutter, H. Hel-

lendoorn, “Centralized versus decentral-

ized route choice control in DCV-based

baggage handling systems”,

Proceedings

of the 2009 IEEE International Confer-

ence on Networking, Sensing and Control

,

Okayama, Japan, March 26-29, 2009.

[7]

R. Scattolini, “Architectures for dis-

tributed and hierarchical model predictive

control – a review”,

Journal of Process

Control

, Vol. 19, pp. 723-731, 2009.

[8]

R. Scattolini, P. Colaneri, “Hierar-

chical Model Predictive Control”,

Pro-

ceedings of the 46

th

IEEE Conference on

Decision and Control

, New Orleans, LA,

USA, Dec. 12-14 2007.

[9]

Institute of Industrial Technology and

Automation (ITIA) – National Research

Council (CNR) de-manufacturing pilot

plant, Italy,

www.itia.cnr.it/en/index.

php?sez=4#notizia42.

[10]

G. Copani, A. Brusaferri, M. Colle-

dani, N. Pedrocchi, M. Sacco, T. Tolio,

“Integrated De-manufacturing systems

as new approach to End-of-Life manage-

ment of mechatronic devices”.

10

th

Global

Conference on Sustainable Manufactur-

ing Towards Implementing Sustainable

Manufacturing

, Istanbul, Turkey, 2012.

[11]

C. Ghezzi, M. Jazayeri, D. Mandri-

oli,

Fundamentals of Software Engineer-

ing

, Prentice Hall, 1991.

[12]

A. Cataldo, R. Scattolini, “Modeling

and model predictive control of a de-man-

ufacturing plant”,

IEEE Multi-conference

on Systems and Control

, Antibes/Nice,

8-10 October 2014.

[13] ICS Triplex ISaGRAF, www.isa- graf.com.

[14]

Simio simulation software,

www. simio.com

[15]

A. Cataldo, M. Taisch. B. Stahl,

Modeling, “Simulation and evaluation of

Energy consumption for a manufacturing

production line”,

39

th

Annual Conference

of IEEE on Industrial Electronics Society

IECON 2013

, Vienna - Austria, 10-13

November 2013.