Supervisory Control of Discrete Event Systems Using Petri Nets

Moody, John; Antsaklis, Panos J.

doi:10.1007/978-1-4615-5711-1

Cited by 292 publications

(178 citation statements)

References 43 publications

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“…Theorem 5.5 shows that existence of a controller for immediately leads to a controller for . Furthermore, when is finite existing supervisory control [10], [25], [30] and controller synthesis [5], [13], [26], [33], [44], [45] techniques can be immediately used for the construction of . In addition to provide a new computational approach to controller synthesis problems for continuous control systems, Theorem 5.5 also shows that it is now possible to design controllers based on specifications that, traditionally, have not been considered for continuous systems such as regular languages, transitions systems, temporal logics, etc.…”

Section: Symbolic Control Based On Symbolic Subsystemsmentioning

confidence: 99%

Symbolic Control of Linear Systems Based on Symbolic Subsystems

Tabuada

2006

IEEE Trans. Automat. Contr.

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Abstract-This paper describes an approach to the control of continuous systems through the use of symbolic models describing the system behavior only at a finite number of points in the state space. These symbolic models can be seen as abstract representations of the continuous dynamics enabling the use of algorithmic controller design methods. We identify a class of linear control systems for which the loss of information incurred by working with symbolic subsystems can be compensated by feedback. We also show how to transform symbolic controllers designed for a symbolic subsystem into controllers for the original system. The resulting controllers combine symbolic controller dynamics with continuous feedback control laws and can thus be seen as hybrid systems. Furthermore, if the symbolic controller already accounts for software/hardware requirements, the hybrid controller is guaranteed to enforce the desired specifications by construction thereby reducing the need for formal verification.

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Section: Symbolic Control Based On Symbolic Subsystemsmentioning

confidence: 99%

Symbolic Control of Linear Systems Based on Symbolic Subsystems

Tabuada

2006

IEEE Trans. Automat. Contr.

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“…This section develops the TPN model of the KRC and its supervisor that is synthesized based on P-invariant method [9], [21].…”

Section: Modelling and Supervision Krc Systems Using Tpnmentioning

confidence: 99%

“…For more details about Petri nets, the readers can be directed to read [5], [16], [21]. The incidence matrix of the plant is…”

Section: Iii3 Supervisory Control Synthesis For the Krc Systemmentioning

confidence: 99%

Modelling and Control of KRC Systems Using TPN and TA

Allaa¹,

Awad²,

Anwar³

et al. 2015

IJAREEIE

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This paper models and supervises kernel railroad crossing (KRC) systems using timed Petri nets and focuses on the control synthesis method, which consists in computing new firing conditions for the timed automaton (TA) transitions so that the forbidden locations are no longer reachable. The system to be modeled and supervised using timed Petri nets is converted to a TA and is analyzed using automata so that the functioning of the system respects given specifications. The main reason of using automata is that although the timed Petri nets supervisor controls the system, it fails to define the time units needed for the undefined time of a transition. Simulation results show the soundness of KRC modeling and supervision in the sense of ensuring the safety and maximizing utility (the gate should be opened as long as possible). KEYWORDS:Automata, Timed automata, discrete event systems, Petri nets, Timed Petri nets, KRC systems I.INTRODUCTIONThe process synchronization and forbidden states avoidance are considered as the main problems in both normal and abnormal modes of automation systems [1]. These characteristics allow the system to be considered as Discrete Event System (DES) and allow the researchers to perform the analysis and control of such systems using Petri nets (PN) and automata. DES is a dynamic system with state evolution produced by the occurrence of physical events. For example, an event is opening or closing a gate in Kernel Railroad Crossing (KRC) systems [2][3]. DES can be found in domains such as manufacturing, robotic, traffic control, logistics, and communication systems, etc.Important contributions in supervisory control of DES based on the Finite Automata (FA) and Petri Nets (PNs) are found in [4][5][6][7][8]. Petri net models are normally more compact than similar automata based models and are better suited for the representation of discrete event systems. They also have a good representational power [9]. On one hand, numerous approaches to the systematic construction of Petri net models have been proposed in [10]. On the other hand, because automata detail the process to be modelled, they are also employed to analysis and avoid forbidden states with ease.Historically, the theory of DES for supervision was initiated by the research work of Ramadge and Wonham [8]. A process is considered to be a spontaneous generator of events. Its functioning is characterized by event sequences. A supervisor is a DES which can modify the functioning of the process in order to avoid forbidden states. Unfortunately, the theory of Ramadge and Wonham does not a maximally permissive in nature [11]. A maximal permissive supervisor is to ensure that the controlled behaviour of the DES must be maximally permissive within a given specifications, i.e. all events which do not contradict the given specifications are allowed to happen. Thus, the theory of Ramadge and Wonham cannot be used to control all DES. An extension of this theory to the control of DES is proposed to force the occurrence of certain events to...

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“…, n, are "design parameters", and their values are chosen such that they guarantee the condition of Equation 68. The selected bounds can be subsequently enforced on the behavior of the original net by the addition of appropriate "monitor places", according to the theory developed in [10]. Finally, it is also known that:…”

Section: Explaining the Functionality Of Algebraic Dapsmentioning

confidence: 99%

Algebraic Deadlock Avoidance Policies for Sequential Resource Allocation Systems

Reveliotis

2007

Engineering and Management Innovation

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As many contemporary technological applications move to operational modes of more extensive and flexible automation, there is a rising need to design and control the underlying resource allocation not only for efficiency, but also for logical correctness and internal consistency. The material presented in this chapter offers a unifying and comprehensive treatment of a class of policies that have been proposed as an effective and efficient solution to this emerging class of logical control problems.

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Supervisory Control of Discrete Event Systems Using Petri Nets

Cited by 292 publications

References 43 publications

Symbolic Control of Linear Systems Based on Symbolic Subsystems

Symbolic Control of Linear Systems Based on Symbolic Subsystems

Modelling and Control of KRC Systems Using TPN and TA

Algebraic Deadlock Avoidance Policies for Sequential Resource Allocation Systems

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