Distributed state estimation in discrete event systems

Xu, Shiping; Kumar, Ratnesh

doi:10.1109/acc.2009.5160029

“…Some other works deal with the computation of a state estimate of a centralized system with distributed controllers. For example, in [39], Xu and Kumar propose a distributed algorithm which computes an estimate of the current state of a system. Local estimators maintain and update local state estimates from their own observation of the system and information received from the other estimators.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, as we consider concurrent systems, we also have to take account the communication delay between sub-systems to compute the state-estimates as well as the control policies. Finally, compared with [39], we have chosen to exchange information between controllers using existing communication channel between subsystems. This renders the computation of the state-estimates completely different.…”

Section: Introductionmentioning

confidence: 99%

Symbolic Supervisory Control of Distributed Systems With Communications

Kalyon

¹

,

Gall

²

,

Marchand

³

et al. 2014

IEEE Trans. Automat. Contr.

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Abstract-We consider the control of distributed systems composed of subsystems communicating asynchronously; the aim is to build local controllers that restrict the behavior of a distributed system in order to satisfy a global state avoidance property. We model distributed systems as communicating finite state machines with reliable unbounded FIFO queues between subsystems. Local controllers can only observe the behavior of their proper subsystem and do not see the queue contents. To refine their control policy, controllers can use the FIFO queues to communicate by piggy-backing extra information (some timestamps and their state estimates) to the messages sent by the subsystems. We provide an algorithm that computes, for each local subsystem (and thus for each controller), during the execution of the system, an estimate of the current global state of the distributed system. We then define a synthesis algorithm to compute local controllers. Our method relies on the computation of (co-)reachable states. Since the reachability problem is undecidable in our model, we use abstract interpretation techniques to obtain overapproximations of (co-)reachable states. An implementation of our algorithms provides an empirical evaluation of our method.

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“…For example, in [25], Xu and Kumar propose a distributed algorithm which computes an estimate of the current state of a system. Local estimators maintain and update local state estimates from their own observation of the system and information received from the other estimators.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of communicating controllers for distributed systems

Kalyon¹,

Gall

²

,

Marchand

³

et al. 2011

IEEE Conference on Decision and Control and European Control Conference

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Abstract-We consider the control of distributed systems composed of subsystems communicating asynchronously; the aim is to build local controllers that restrict the behavior of a distributed system in order to satisfy a global state avoidance property. We model our distributed systems as communicating finite state machines with reliable unbounded FIFO queues between subsystems. Local controllers can only observe their proper local subsystems and do not observe the queues. To refine their control policy, they can use the FIFO queues to communicate by piggybacking extra information to the messages sent by the subsystems. We define synthesis algorithms allowing to compute the local controllers. We explain how we can ensure the termination of this control algorithm by using abstract interpretation techniques, to overapproximate queue contents by regular languages. An implementation of our algorithms provides an empirical evaluation of our method. I. INTRODUCTIONIn the framework of control of distributed systems, two classes of systems are generally considered, depending on whether the communications between subsystems are synchronous or not. When the synchrony hypothesis [3] can be made, the decentralized control problem and the modular control problem address the design of coordinated controllers that jointly ensure the desired properties for this kind of systems [26], [22], [21], [9], [13]. When considering asynchronous distributed systems, one have to take into account some communication delays between the components of the system, which renders the distributed control problem much harder even undecidable [24].We are here interested in the second problem i.e., the distributed control problem. Our aim is to solve this problem when the system to be controlled is composed of n subsystems that asynchronously communicate through reliable unbounded FIFO channels (or queues). These subsystems are modeled by communicating finite state machines [5] (CFSM for short) that explicitly express the work and communications of a distributed system. This model appears to be essential for concurrent systems in which components cooperate via asynchronous message passing through unbounded buffers (they are e.g. widely used to model communication protocols). We thus assume that the distributed system is already built and the architecture of communication between the different subsystems is fixed. Following the architecture described in Figure 1, we assume that each subsystem is

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“…We however use the same symbolic representation of queue contents as in [2,12]. In [22], Kumar and Xu propose a distributed algorithm which computes an estimate of the current state of a system. More precisely, local estimators maintain and update local state estimates from their own observation of the system and information received from the other estimators.…”

Section: Introductionmentioning

confidence: 99%