Control Architecture for Reconfigurable Manufacturing Systems: the PABADIS'PROMISE approach

Ferrarini, Luca; Veber, C.; Lüder, Arndt; Peschke, J.; Καλογεράς, Αθανάσιος; Gialelis, John; Rode, Jochen; Wunsch, D.; Chapurlat, Vincent

doi:10.1109/etfa.2006.355427

Cited by 36 publications

(16 citation statements)

References 12 publications

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“…Typically, several European projects addressed the design of distributed/hybrid control architectures into the so-called "smart factories". PABADIS (Lüder et al 2004) and PABADIS PROMISE (Ferrarini et al 2006) are among the firsts EU projects in that direction. More recently, let's mention GRACE 1 , SMARTPRODUCT 2 and ARUM 3 projects.…”

Section: Insights and Limits Of The Literature Facing This Challengementioning

confidence: 98%

Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges

et al. 2015

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International audienceNowadays, industrials are seeking for models and methods that are not only able to provide efficient overall production performance, but also for reactive systems facing a growing set of unpredicted events. One important research activity in that field focuses on holonic/multi-agent control systems that couple predictive/proactive and reactive mechanisms into agents/holons. Meanwhile, not enough attention is paid to the optimization of this coupling. The aim of this paper is to depict the main research challenges that are to be addressed before expecting a large industrial dissemination. Relying on an extensive review of the state of the art, three main challenges are highlighted: the estimation of the future performances of the system in reactive mode, the design of efficient switching strategies between predictive and reactive modes and the design of efficient synchronization mechanisms to switch back to predictive mode. indicator of the production system, discrete-event observer, flexible manufacturing system

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Section: Insights and Limits Of The Literature Facing This Challengementioning

confidence: 98%

Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges

et al. 2015

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“…Research approaches in the area of changeable manufacturing systems follow various directions: flexibility and reconfigurability concerning preplanned changes and easy integration of new components (Koren et al 1999;ElMaraghy 2006); agentified modules for mini-systems containing all the information they need for their execution (Hollis et al 2003;Hanisch and Munz 2008); bio-inspired techniques such as holonic architectures for optimising shop-floor production (Valckenaers and Van Brussel 2005) or DNA-inspired techniques where the product to be assembled contains the knowledge about how it must be assembled (similar to DNA information) (Ueda 2006); exploiting autonomous software agents for coping with changes during production (Ferrarini et al 2006), dynamic agent coalitions for production planning (Pechoucek et al 2000), or for learning how to coordinate and adapt to production (Shen et al 1998). …”

Section: Introductionmentioning

confidence: 99%

Ambient intelligence in self-organising assembly systems using the chemical reaction model

Frei

Serugendo

Şerbănuţă

2010

J Ambient Intell Human Comput

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This article discusses self-organising assembly systems (SOAS), a type of assembly systems that (1) participate in their own design by spontaneously organising themselves in response to the arrival of a product order and (2) manage themselves during production. SOAS address the industry's need for agile manufacturing systems to be highly responsive to market dynamics. Manufacturing systems need to be easily and rapidly changeable, but system re-engineering/reconfiguration and especially their (re-)programming are manual, work-intensive and errorprone procedures. With SOAS, we try to facilitate this by giving the systems gradually more self-* capabilities. SOAS eases the work of the SOAS designer and engineer when designing such as system for a specific product, and supports the work of the SOAS operator when supervising the system during production. SOAS represent an application domain of ambient intelligence and humanised computing which is not frequently considered, but therefore none the less important. This article explains how an SOAS produces its own design as the result of a self-organising process following the Chemical Abstract Machine (CHAM) paradigm: industrial robots self-assemble according to specific chemical rules in response to a product order. This paper reports on SOAS in general, the specification of the chemical reactions and their simulation in Maude.

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“…Some research into multi-agent systems in production environments has been done e.g. by the Plant Automation BAsed on DIstributed Systems (PABADIS) [7,12,11] or the Holonic Manufacturing Systems (HMS) [10]. Another proposal emanates from the project ServiceOriented Crosslayer infRAstructure for Distributed smart Embedded deviceS (SOCRADES) [3,4,2] which introduces a service oriented architecture for using automation services.…”

Section: Introductionmentioning

confidence: 99%

Microprogrammable hardware abstraction layer for flexible automation

Schmidtmann

Kreutz

Barkhoff³

et al. 2009

2009 IEEE Conference on Emerging Technologies &Amp; Factory Automation

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Nowadays, one important goal of automation is to optimize the complete work and material flow under the constraints of energy consumption, quality and throughput. As a matter of fact, the whole control process should be flexible and reconfigurable which targets the three levels of the control pyramid (ERP, MES, Field). In the context of our ongoing research project LK 3 S an agent platform is under construction which enables optimization and flexibility. This paper discusses the design and development of a hardware abstraction layer for the lowest level of the control pyramid which offers all services needed by the agent platform and which allows links between work orders and tasks of the field level, e.g. a robot has to mount a tire. Field level tasks are elementary commands composed of a sequence of atomic operations. These tasks play a similar role as microcode for CPUs. Since every task operates individually it can be scheduled so that multiple concurrent actions can be serialized in a way that real-time requirements are still met. Even at runtime, new tasks can be dynamically defined, transfered, activated and deleted within the field level.

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Control Architecture for Reconfigurable Manufacturing Systems: the PABADIS'PROMISE approach

Cited by 36 publications

References 12 publications

Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges

Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges

Ambient intelligence in self-organising assembly systems using the chemical reaction model

Microprogrammable hardware abstraction layer for flexible automation

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