7,591 research outputs found
Modeling and analysis using hybrid Petri nets
This paper is devoted to the use of hybrid Petri nets (PNs) for modeling and
control of hybrid dynamic systems (HDS). Modeling, analysis and control of HDS
attract ever more of researchers' attention and several works have been devoted
to these topics. We consider in this paper the extensions of the PN formalism
(initially conceived for modeling and analysis of discrete event systems) in
the direction of hybrid modeling. We present, first, the continuous PN models.
These models are obtained from discrete PNs by the fluidification of the
markings. They constitute the first steps in the extension of PNs toward hybrid
modeling. Then, we present two hybrid PN models, which differ in the class of
HDS they can deal with. The first one is used for deterministic HDS modeling,
whereas the second one can deal with HDS with nondeterministic behavior.
Keywords: Hybrid dynamic systems; D-elementary hybrid Petri nets; Hybrid
automata; Controller synthesi
Modeling and Analysis of Hybrid Dynamic Systems Using Hybrid Petri Nets
International audienceWe consider in this paper the extensions of the PN formalism in the direction of hybrid modeling. We briefly presents hybrid dynamic systems. Then the hybrid automata model. Is introduced and we discuss continuous Petri nets. These models are obtained from discrete PNs by the fluidification of the markings. They constitute the first steps in the extension of PNs toward hybrid modeling. Two hybrid PN models, which differ in the class of HDS they can deal with, are studied. The first one is used for deterministic HDS modeling, whereas the second one can deal with HDS with nondeterministic behavior. The general control structure based on hybrid PNs is briefly described
Scheduling and discrete event control of flexible manufacturing systems based on Petri nets
A flexible manufacturing system (FMS) is a computerized production system that can simultaneously manufacture multiple types of products using various resources such as robots and multi-purpose machines. The central problems associated with design of flexible manufacturing systems are related to process planning, scheduling, coordination control, and monitoring. Many methods exist for scheduling and control of flexible manufacturing systems, although very few methods have addressed the complexity of whole FMS operations. This thesis presents a Petri net based method for deadlock-free scheduling and discrete event control of flexible manufacturing systems. A significant advantage of Petri net based methods is their powerful modeling capability. Petri nets can explicitly and concisely model the concurrent and asynchronous activities, multi-layer resource sharing, routing flexibility, limited buffers and precedence constraints in FMSs. Petri nets can also provide an explicit way for considering deadlock situations in FMSs, and thus facilitate significantly the design of a deadlock-free scheduling and control system.
The contributions of this work are multifold. First, it develops a methodology for discrete event controller synthesis for flexible manufacturing systems in a timed Petri net framework. The resulting Petri nets have the desired qualitative properties of liveness, boundedness (safeness), and reversibility, which imply freedom from deadlock, no capacity overflow, and cyclic behavior, respectively. This precludes the costly mathematical analysis for these properties and reduces on-line computation overhead to avoid deadlocks. The performance and sensitivity of resulting Petri nets, thus corresponding control systems, are evaluated. Second, it introduces a hybrid heuristic search algorithm based on Petri nets for deadlock-free scheduling of flexible manufacturing systems. The issues such as deadlock, routing flexibility, multiple lot size, limited buffer size and material handling (loading/unloading) are explored. Third, it proposes a way to employ fuzzy dispatching rules in a Petri net framework for multi-criterion scheduling. Finally, it shows the effectiveness of the developed methods through several manufacturing system examples compared with benchmark dispatching rules, integer programming and Lagrangian relaxation approaches
Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence
Process discovery techniques return process models that are either formal
(precisely describing the possible behaviors) or informal (merely a "picture"
not allowing for any form of formal reasoning). Formal models are able to
classify traces (i.e., sequences of events) as fitting or non-fitting. Most
process mining approaches described in the literature produce such models. This
is in stark contrast with the over 25 available commercial process mining tools
that only discover informal process models that remain deliberately vague on
the precise set of possible traces. There are two main reasons why vendors
resort to such models: scalability and simplicity. In this paper, we propose to
combine the best of both worlds: discovering hybrid process models that have
formal and informal elements. As a proof of concept we present a discovery
technique based on hybrid Petri nets. These models allow for formal reasoning,
but also reveal information that cannot be captured in mainstream formal
models. A novel discovery algorithm returning hybrid Petri nets has been
implemented in ProM and has been applied to several real-life event logs. The
results clearly demonstrate the advantages of remaining "vague" when there is
not enough "evidence" in the data or standard modeling constructs do not "fit".
Moreover, the approach is scalable enough to be incorporated in
industrial-strength process mining tools.Comment: 25 pages, 12 figure
Analysis of signalling pathways using the prism model checker
We describe a new modelling and analysis approach for signal
transduction networks in the presence of incomplete data. We illustrate
the approach with an example, the RKIP inhibited ERK pathway
[1]. Our models are based on high level descriptions of continuous time
Markov chains: reactions are modelled as synchronous processes and concentrations
are modelled by discrete, abstract quantities. The main advantage
of our approach is that using a (continuous time) stochastic logic
and the PRISM model checker, we can perform quantitative analysis of
queries such as if a concentration reaches a certain level, will it remain at
that level thereafter? We also perform standard simulations and compare
our results with a traditional ordinary differential equation model. An
interesting result is that for the example pathway, only a small number
of discrete data values is required to render the simulations practically
indistinguishable
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