Event-driven model predictive control of timed hybrid Petri nets

Júlvez, Jorge; Cairano, Stefano Di; Bemporad, Alberto; Mahulea, Cristian

doi:10.1002/rnc.2958

Cited by 13 publications

(5 citation statements)

References 30 publications

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“…Using systems and control theory to analyze and influence PNs is not uncommon in the literature [41,42], and also MPC techniques are used [43][44][45]. In the context of MPC for PNs, usually either continuous or hybrid PNs are considered from the start, or fluidification of the Petri Net is used, meaning that the integer constraint is relaxed and the tokens are considered to be a fluid.…”

Section: Model Predictive Controlmentioning

confidence: 99%

Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0

Wenzelburger

Allgöwer

2021

Applied Sciences

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In the context of Industry 4.0, flexible manufacturing systems play an important role. They are designed to provide the possibility to adapt the production process by reacting to changes and enabling customer specific products. The versatility of such manufacturing systems, however, also needs to be exploited by advanced control strategies. To this end, we present a novel scheduling scheme that is able to flexibly react to changes in the manufacturing system by means of Model Predictive Control (MPC). To introduce flexibility from the start, the initial scheduling problem, which is very general and covers a variety of special cases, is formulated in a modular way. This modularity is then preserved during an automatic transformation into a Petri Net formulation, which constitutes the basis for the two presented MPC schemes. We prove that both schemes are guaranteed to complete the production problem in closed loop when reasonable assumptions are fulfilled. The advantages of the presented control framework for flexible manufacturing systems are that it covers a wide variety of scheduling problems, that it is able to exploit the available flexibility of the manufacturing system, and that it allows to prove the completion of the production problem.

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Section: Model Predictive Controlmentioning

confidence: 99%

Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0

Wenzelburger

Allgöwer

2021

Applied Sciences

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“…26, see e.g. Júlvez et al (2014). Note that q(k) accounts for the time interval between two consecutive events while t the elapsed time.…”

Section: Train Gate Model and Constraintsmentioning

confidence: 99%

A receding horizon event-driven control strategy for intelligent traffic management

Lúcia

Franzè

Famularo

2021

Discrete Event Dyn Syst

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In this paper, the intelligent traffic management within a smart city environment is addressed by developing an ad-hoc model predictive control strategy based on an event-driven formulation. To this end, a constrained hybrid system description is considered for safety verification purposes and a low-demanding receding horizon controller is then derived by exploiting set-theoretic arguments. Simulations are performed on the train-gate benchmark system to show the effectiveness and benefits of the proposed methodology.

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“…However, hybrid automata do not provide a suitable programming model for implementation. Related modeling paradigms, such as Petri nets [11,12] and Modelica [13], are mainly used for analysis and verification.…”

Section: Related Workmentioning

confidence: 99%

An Event-Based Programming Model with Geometric Spatial Semantics For Cyber-Physical Production Systems

Wan

Zeng

2020

Applied Sciences

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With the increasing complexities, such as massive computing devices and strict requirements for collaboration, of industrial production systems, the concept of the cyber-physical production system (CPPS) is considered as a promising approach for addressing these challenges. However, programmability is a challenge in CPPSs. The traditional chimney style of programming requires considerable design effort from engineers to handle the spatiotemporal heterogeneity of cyber-physical model that encompasses computing, temporal, and geometric semantics along with physical dynamics. Thus, an easy-to-use programming model and an integrated programming framework that are suited to this context are required. However, the existing programming models typically fully consider only computing while only partially considering temporal characteristics, and they rarely consider geometric semantics. To solve this problem, this paper proposes a novel event-based programming model, GePro, considering the geometric spatial semantics to realize integrated programming and reduce design effort, especially the reconfiguration of adding new physical devices into the exist system. A prototype of GePro is implemented and verified based on IEC 61499 by the implementation of design and reconfigure assembly CPPS . The results show that using GePro results in a programming time savings of an development life cycle time compared to traditional models for reconfiguration.

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Event-driven model predictive control of timed hybrid Petri nets

Cited by 13 publications

References 30 publications

Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0

Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0

A receding horizon event-driven control strategy for intelligent traffic management

An Event-Based Programming Model with Geometric Spatial Semantics For Cyber-Physical Production Systems

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