15 research outputs found

    Explorando o uso de distinguidores e de autômatos finitos estendidos na teoria do controle supervisório de sistemas a eventos discretos

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    Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2013.Esta tese de doutorado investiga dois aspectos relevantes na Teoria do Controle Supervisório (TCS) de Sistemas a Eventos Discretos (SEDs): (i) o grau de dificuldade enfrentado ao modelar especificações para serem cumpridas pelo sistema sob controle; (ii) a complexidade computacional do procedimento de síntese de uma solução para o Problema do Controle Supervisório (PCS). Para tratar desses aspectos, são propostas duas diferentes abordagens: o uso de Distinguidores e o uso de Autômatos Finitos Estendidos (AFEs). A abordagem com distinguidores consiste em refinar o conjunto original de eventos do modelo de um SED, em um novo conjunto. Cada refinamento é apropriadamente escolhido para identificar uma instância particular em que o evento original ocorre no sistema. Então, um mapa chamado Distinguidor é proposto para estabelecer a relação entre as cadeias dos alfabetos original e refinado. Fazendo-se uso de eventos refinados, pode-se simplificar a modelagem de especificações de controle cuja representação seria bastante complexa no alfabeto original. Além disso, é mostrado que resolver o PCS usando distinguidores leva diretamente à solução ótima de controle, porém, sem vantagens computacionais na síntese em relação ao método não-refinado. Nesse sentido, é também mostrado como construir aproximações para o modelo refinado de um SED. Ao serem usadas na síntese de supervisores, as aproximações permitem reduzir o custo computacional do procedimento, ao mesmo tempo em que permitem preservar a controlabilidade, a máxima permissividade e o não-bloqueio da solução de controle. A segunda proposta a ser apresentada consiste em modelar um SED através de AFEs. Os AFEs são estruturas de estados cujas transições são estendidas com fórmulas que atualizam variáveis de tal modo que seus valores passam a fazer parte dos estados. Assim, fazendo-se uso de valores de variáveis, pode-se facilmente expressar a semântica de uma especificação através de condições lógicas, implementadas sobre o modelo de um SED. Mostra-se que o uso de AFEs leva diretamente à solução ótima para o PCS a qual, no entanto, é obtida sem vantagens computacionais, em relação ao método convencional. Isso ocorre, porque os valores das variáveis, ainda que implícitos no modelo do sistema, precisam ser considerados na síntese, o que elimina possíveis ganhos trazidos pela simplificação da modelagem. Nesse sentido, propõe-se um método para abstrair certas variáveis no AFE que modela um SED. Ao serem usadas na síntese, tais abstrações reduzem o custo computacional do procedimento, ao mesmo tempo em que permitem preservar a controlabilidade, a máxima permissividade e o não-bloqueio da solução de controle. Um algoritmo para obter supervisores, a partir de AFEs, também é apresentado e ilustrado por meio de um exemplo. Sempre que possível, as duas abordagens propostas são comparadas e ilustradas por exemplos. Em particular, o exemplo de um sistema de manufatura é adotado ao longo da tese para permitir a análise dos diferentes métodos de síntese. Como contribuição final, propõe-se associar o uso de distinguidores a um método descentralizado de síntese, em particular ao Controle Modular Local (CML). Inicialmente, mostra-se que o uso direto de distinguidores, em geral, complexifica a resolução do CML. Essa inconveniência é mitigada pela síntese combinada, um método por meio do qual supervisores locais são obtidos usando distinguidores apenas quando apropriado. Esse método leva a um comportamento global controlado que é equivalente ao CML original, porém, que também é processado com equivalente custo computacional. Nesse sentido, mostra-se ainda como combinar vantagens do CML, de distinguidores e de aproximações. O mesmo exemplo do sistema de manufatura ilustra essa contribuição final.<br

    Combining advantages from parameters in modeling and control of discrete event systems

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    Although Finite-State Automata (FSA) have been successfully used in modeling and control of Discrete Event Systems (DESs), they are limited to represent complex and advanced features of DESs, such as context recognition and switching. The literature has suggested that a FSA can nevertheless be enriched with parameters properly collected from the modeled system, so that this favors design and control. A parameter can be embedded either on transitions or states. However, each approach is structured within a specific framework, so that their comparison and integration are not straightforward and they may lead to different control solutions, modeled, computed and implemented using distinct strategies. In this paper, we show how to combine advantages from parameters in modeling and control of DESs. Each approach is structured and their advantages are identified and exemplified. Then, we propose a conversion method that allows to translate a design-friendly model into a synthesis-efficient structure. Examples illustrate the approach.CNPq, under grant number 402145/2016-0, 09, Araucaria Foundation, CAPES, and FINEP, and partially supported by ERDF - The European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme, and by National Funds through FCT - Fundação para a Ciência e a Tecnologia, within project POCI- ˆ 01-0145-FEDER-030947 (KLEE

    Multi-resolution fault diagnosis in discrete-event systems

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    In this thesis, a framework for multi-resolution fault diagnosis in discrete-event systems (DES) is introduced. Here a sequence of plant models, with increasing resolution, are used in fault diagnosis and the range of possible diagnosis is narrowed down step by step, until the failure node is isolated. In this way, the original problem of fault diagnosis is replaced by a sequence of smaller problems. The plant models used at each step of diagnosis are abstractions of the original plant model. We propose to use model reduction through the solutions of the Relational Coarsest Partition problem to obtain these abstractions. For each diagnosis step, minimal sensor sets are chosen to have a coarser output map, and hence, to improve the efficiency of model reduction. In this thesis, a polynomial algorithm is proposed that verifies failure diagnosability by examining the distinguishability of two plant (normal/faulty) conditions at a time. A procedure is presented that finds minimal sensor sets, referred to as minimal distinguishes for distinguishability of one condition from another. A polynomial procedure is introduced that combines minimal distinguishers to obtain a minimal sensor set for fault diagnosis. The proposed method reduces the computational complexity of sensor selection. A benefit of using minimal distinguishers is that their computation maybe speeded up using expert knowledge. The proposed method for sensor selection is particularly suitable for multi-resolution diagnosis since it permits some of the results of computations, performed for sensor selection at the lowest (finest) level of multi-resolution diagnosis to be reduced at higher levels. This feature is particularly useful in reducing the computations necessary for online reconfiguration of the multi-resolution diagnosis system. An important procedure used in sensor selection is testing diagnosability. In this thesis, a new procedure for testing diagnosability in timed DES is introduced based on the relatively timing of plant output sequence. It is shown through example that the proposed test maybe executed with significantly fewer computations compared to tests developed for untimed models and adapted for timed systems. Furthermore, two new sets of sufficient conditions are provided under which diagnoser design and diagnosability tests based on relative timing of output sequence can be performed efficientl

    Structuring Multilevel Discrete-Event Systems With Dependence Structure Matrices

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    Despite the correct-by-construction property, one of the major drawbacks of supervisory control synthesis is state-space explosion. Several approaches have been proposed to overcome this computational difficulty, such as modular, hierarchical, decentralized, and multilevel supervisory control synthesis. Unfortunately, the modeler needs to provide additional information about the system's structure or controller's structure as input for most of these nonmonolithic synthesis procedures. Multilevel synthesis assumes that the system is provided in a tree-structured format, which may resemble a system decomposition. In this paper, we present a systematic approach to transform a set of plant models and a set of requirement models provided as extended finite automata into a tree-structured multilevel discrete-event system to which multilevel supervisory control synthesis can be applied. By analyzing the dependencies between the plants and the requirements using dependence structure matrix techniques, a multilevel clustering can be calculated. With the modeling framework of extended finite automata, plant models and requirements depend on each other when they share events or variables. We report on experimental results of applying the algorithm's implementation on several models available in the literature to assess the applicability of the proposed method. The benefit of multilevel synthesis based on the calculated clustering is significant for most large-scale systems

    SYNTHESIS EQUIVALENCE OF TRIPLES

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    This working paper describes a framework for compositional supervisor synthesis, which is applicable to all discrete event systems modelled as a set of deterministic automata. Compositional synthesis exploits the modular structure of the input model, and therefore works best for models consisting of a large number of small automata. The state-space explosion is mitigated by the use of abstraction to simplify individual components, and the property of synthesis equivalence guarantees that the final synthesis result is the same as it would have been for the non-abstracted model. The working paper describes synthesis equivalent abstractions and shows their use in an algorithm to efficiently compute supervisors. The algorithm has been implemented in the DES software tool Supremica and successfully computes nonblocking modular supervisors, even for systems with more than 1014 reachable states, in less than 30 seconds

    Synthesis of least restrictive controllable supervisors for extended finite-state machines with variable abstraction

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    This paper presents an algorithm that combines modular synthesis for extended finite-state machines (EFSM) with abstraction of variables by symbolic manipulation, in order to compute least restrictive controllable supervisors. Given a modular EFSM system consisting of several components, the proposed algorithm synthesises a separate supervisor for each specification component. To synthesise each supervisor, the algorithm iteratively selects components (plants and variables) from a synchronous composition until a least restrictive controllable solution is obtained. This improves on previous results of the authors where abstraction is only performed by the selection of components and not variables. The paper explains the theory of EFSM synthesis and abstraction and its algorithms. An example of a flexible manufacturing system illustrates how the proposed algorithm works to compute a modular supervisor

    Supervisory control synthesis for large-scale infrastructural systems

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    Supervisory control synthesis for large-scale infrastructural systems

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    On Compositional Approaches for Discrete Event Systems Verification and Synthesis

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    Over the past decades, human dependability on technical devices has rapidly increased.Many activities of such devices can be described by sequences of events,where the occurrence of an event causes the system to go from one state to another.This is elegantly modelled by state machines. Systems that are modelledin this way are referred to as discrete event systems. Usually, these systems arehighly complex, and appear in settings that are safety critical, where small failuresmay result in huge financial and/or human losses. Having a control functionis one way to guarantee system correctness.The work presented in this thesis concerns verification and synthesis of suchsystems using the supervisory control theory proposed by Ramadge and Wonham. Supervisory control theory provides a general framework to automaticallycalculate control functions for discrete event systems. Given a model of thesystem, the plant to be controlled, and a specification of the desired behaviour,it is possible to automatically compute, i.e. synthesise, a supervisor that ensuresthat the specification is satisfied.Usually, systems are modular and consist of several components interactingwith each other. Calculating a supervisor for such a system in the straightforwardway involves constructing the complete model of the considered system, whichmay lead to the inherent complexity problem known as the state-space explosionproblem. This problem occurs as the number of states grows exponentially withthe number of components, which makes it intractable to examine the globalstates of a system due to lack of memory and time.One way to alleviate the state-space explosion problem is to use a compositionalapproach. A compositional approach exploits the modular structure of asystem to reduce the size of the model. This thesis mainly focuses on developingabstraction methods for the compositional approach in a way that the finalverification and synthesis results are the same as it would have been for the nonabstractedsystem. The algorithms have been implemented in the discrete eventsystem software tool Supremica and have been applied to verify and computememory efficient supervisors for several large industrial models

    On Compositional Approaches for Discrete Event Systems Verification and Synthesis

    Get PDF
    Over the past decades, human dependability on technical devices has rapidly increased.Many activities of such devices can be described by sequences of events,where the occurrence of an event causes the system to go from one state to another.This is elegantly modelled by state machines. Systems that are modelledin this way are referred to as discrete event systems. Usually, these systems arehighly complex, and appear in settings that are safety critical, where small failuresmay result in huge financial and/or human losses. Having a control functionis one way to guarantee system correctness.The work presented in this thesis concerns verification and synthesis of suchsystems using the supervisory control theory proposed by Ramadge and Wonham. Supervisory control theory provides a general framework to automaticallycalculate control functions for discrete event systems. Given a model of thesystem, the plant to be controlled, and a specification of the desired behaviour,it is possible to automatically compute, i.e. synthesise, a supervisor that ensuresthat the specification is satisfied.Usually, systems are modular and consist of several components interactingwith each other. Calculating a supervisor for such a system in the straightforwardway involves constructing the complete model of the considered system, whichmay lead to the inherent complexity problem known as the state-space explosionproblem. This problem occurs as the number of states grows exponentially withthe number of components, which makes it intractable to examine the globalstates of a system due to lack of memory and time.One way to alleviate the state-space explosion problem is to use a compositionalapproach. A compositional approach exploits the modular structure of asystem to reduce the size of the model. This thesis mainly focuses on developingabstraction methods for the compositional approach in a way that the finalverification and synthesis results are the same as it would have been for the nonabstractedsystem. The algorithms have been implemented in the discrete eventsystem software tool Supremica and have been applied to verify and computememory efficient supervisors for several large industrial models
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