H2CM: A holonic architecture for flexible hybrid control systems

Several studies typically arise from the interaction of discrete planning algorithms or control and continuous processes, normally called hybrid control systems. It consists in three distinct levels, the controller, the plant and the interface. Hybrid control systems are conventionally modeled by switching patterns using the whole system instead of atomic resource. Therefore, the reconfiguration process is complex because it must take into account the system as a whole, making the hybrid control systems inflexible and more susceptible to uncertainties. The need for flexibility thus leads several teams to investigate the application of holonic paradigm to hybrid control systems. The objective of this paper is to demonstrate the possibility to apply almost directly a holonic discrete-event based reference architecture to hybrid control systems. A case study of industrial electricity generation process was taken, specifically a combined cycle plant (CCP) for verifying the proper operation of the proposed architecture.

[1]  Damien Trentesaux,et al.  ORCA-FMS: a dynamic architecture for the optimized and reactive control of flexible manufacturing scheduling , 2014, Comput. Ind..

[2]  Yves Sallez.  Proposition of an Analysis Framework to Describe the "Activeness" of a Product during Its Life Cycle - Part I: Motivations and Modelling , 2014, Service Orientation in Holonic and Multi-Agent Manufacturing and Robotics.

[3]  P. Kundur,et al.  Dynamics models for combines cycle plants in power system studies , 1994 .

[4]  A. Nerode,et al.  Hybrid Control Systems: An Introductory Discussion to the Special Issue , 1998, IEEE Trans. Autom. Control..

[5]  André Thomas,et al.  The performance of product-driven manufacturing control: An emulation-based benchmarking study , 2009, Comput. Ind..

[6]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[7]  Radu F. Babiceanu,et al.  Development and Applications of Holonic Manufacturing Systems: A Survey , 2006, J. Intell. Manuf..

[8]  Paul Valckenaers and Handrik Van Brussel.  Design for the Unexpected : From Holonic Manufacturing Systems Towards A Humane Mechatronics Society - 978-0-12-803662-4 , 2015 .

[9]  Paulo Leitão,et al.  ADACOR: A holonic architecture for agile and adaptive manufacturing control , 2006, Comput. Ind..

[10]  Paul Valckenaers,et al.  Interacting Holons in Evolvable Execution Systems: The NEU Protocol , 2015, HoloMAS.

[11]  Luc Bongaerts,et al.  Reference architecture for holonic manufacturing systems: PROSA , 1998 .

[12]  Duncan McFarlane,et al.  Holonic manufacturing systems in continuous processing: concepts and control requirements , 1995 .

[13]  Flor Narciso,et al.  An integration architecture for the automation of a continuous production complex. , 2002, ISA transactions.

[14]  José Barbosa,et al.  Self-organized Holonic Manufacturing Systems Combining Adaptation and Performance Optimization , 2012, DoCEIS.

[15]  Seng Kwong Chong,et al.  Intelligent products: From lifecycle data acquisition to enabling product-related services , 2009, Comput. Ind..

[16]  Reha Uzsoy,et al.  Predictable scheduling of a job shop subject to breakdowns , 1998, IEEE Trans. Robotics Autom..

[17]  W. I. Rowen,et al.  Simplified Mathematical Representations of Heavy-Duty Gas Turbines , 1983 .

[18]  P.J. Antsaklis,et al.  Supervisory control of hybrid systems , 2000, Proceedings of the IEEE.

[19]  Hendrik Van Brussel,et al.  Design for the Unexpected , 2015 .

[20]  Duncan C. McFarlane,et al.  A holonic component-based approach to reconfigurable manufacturing control architecture , 2000, Proceedings 11th International Workshop on Database and Expert Systems Applications.

[21]  Pierre Castagna,et al.  Product Specification for Flexible Workflow Orchestrations in Service Oriented Holonic Manufacturing Systems , 2014, Service Orientation in Holonic and Multi-Agent Manufacturing and Robotics.

[22]  José Barbosa,et al.  Dynamic self-organization in holonic multi-agent manufacturing systems: The ADACOR evolution , 2015, Comput. Ind..

[23]  Reha Uzsoy,et al.  Executing production schedules in the face of uncertainties: A review and some future directions , 2005, Eur. J. Oper. Res..

[24]  Jo Wyns,et al.  Reference architecture for holonic manufacturing systems, the key to support evolution and reconfiguration , 1999 .

[25]  José Luis Cuesta Gómez,et al.  Propuesta para la planificación de servicios y programas para personas con TEA , 2012 .

[26]  Luc Bongaerts,et al.  Designing Holonic manufacturing systems , 1998 .

[27]  B. Vahidi,et al.  An Educational Guide to Extract the Parameters of Heavy Duty Gas Turbines Model in Dynamic Studies Based on Operational Data , 2009, IEEE Transactions on Power Systems.

[28]  William I. Rowen,et al.  Simplified Mathematical Representations of Single Shaft Gas Turbines in Mechanical Drive Service , 1992 .

[29]  Duncan C. McFarlane,et al.  A distributed architecture for reconfigurable control of continuous process operations , 2008, J. Intell. Manuf..