Intelligent distributed control systems

Khalgui, Mohamed; Mosbahi, Olfa

doi:10.1016/j.infsof.2010.06.001

Cited by 19 publications

(6 citation statements)

References 20 publications

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“…Khalgui and Mosbahi [20] extend the concept of adding and removing FB in order to allow the relocation of FBs into other controllers. This relocation is based on moving the code of the FB as well as its execution data (its state).…”

Section: Related Workmentioning

confidence: 99%

Agent-based middleware architecture for reconfigurable manufacturing systems

Priego

Iriondo

Gangoiti

et al. 2017

Int J Adv Manuf Technol

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Modern manufacturing systems are expected to be flexible and efficient in order to cope with challenging market demands. Thus, they must be flexible enough as to meet changing requirements such as changes in production, energy efficiency, performance optimization, fault tolerance to process or controller faults, among others. Demanding requirements can be defined as a set of quality of service (QoS) requirements to be met. This paper proposes a generic and customizable multi-agent architecture that, making use of distributed agents, monitors QoS, triggering, if needed, a reconfiguration of the control system to recover QoS. As a proof of concept, the architecture has been implemented to provide availability of the control system understood as service continuity. The prototype has been tested in a case study consisting of an assembly cell where assessment of the approach has been conducted.

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Section: Related Workmentioning

confidence: 99%

Agent-based middleware architecture for reconfigurable manufacturing systems

Priego

Iriondo

Gangoiti

et al. 2017

Int J Adv Manuf Technol

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“…Along with the continuous development of computer and network technology, distributed control systems (DCSs) have gradually become a new trend and attract much attention [1,2]. Compared with traditional point-to-point control systems, DCSs have advantages such as high reliability, reduced weight, low cost, and ease of maintenance [3,4].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Unmeasured Parameters Estimation for Distributed Control Systems

et al. 2020

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The problem of unmeasured parameters estimation for distributed control systems is studied in this paper. The Takagi–Sugeno fuzzy model which can appropriate any nonlinear systems is employed, and based on the model, an observer-based fuzzy H∞ filter which has robustness against time-delay, external noise, and system uncertainties is designed. The sufficient condition for the existence of the desired filter is derived in terms of linear matrix inequalities (LMIs) solutions. Moreover, the underdetermined estimation problem in which the number of sensors available is typically less than the number of state variables to be estimated is specifically addressed. A systematic method is proposed to produce a model tuning parameter vector of appropriate dimension for the estimation of the filter, and the optimal transformation matrix is selected via iterative solution to minimize the estimated error. Finally, a simulation example for turbofan aeroengine is given to illustrate the effectiveness of the proposed method, and the estimated error is less than 2.5%.

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“…Those two solutions satisfy human invisibility constraint in pervasive computing and they reinforce techniques for context awareness computing by getting specific control behaviour for the DIS depending on system execution context [20]. Wang et al [9] and Khalgui et al [31] focus their research work in the reconfiguration of periodic and aperiodic tasks which are triggered by simple events. Nevertheless, no one in our community [2]- [5], [14], [46]- [48] has address the problem of the reconfiguration of intelligent tasks which are triggered by a rule engine as an AI component.…”

mentioning

confidence: 97%

“…Dealing with synchronization and coordination, many research works have used different protocols and techniques to ensure those constraints. MAS is used for this field in Khalgui's work [31] by providing a reconfiguration agent for each device of the DIS to manage the reconfiguration process and a coordination agent to ensure the feasibility of distributed reconfiguration scenarios. This solution didn't study the specificity of the rule based systems where interconnected devices may lead to critical problems and inconsistent decisions such as incoherent rule base.…”

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confidence: 99%

Dynamic Reconfiguration of Intelligence for High Behaviour Adaptability of Autonomous Distributed Discrete-Event Systems

Gharsellaoui

Khalgui

2019

IEEE Access

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This paper deals with the intelligence adaptation of distributed real-time embedded control systems when scenarios of reconfiguration happen in their hardware or software level. The reconfiguration process is a composition of controllers reconfiguration by adding/deleting or updating tasks and intelligence reconfiguration by adding/deleting and updating the rule base. The system architecture is composed of an application layer implemented as the real-time periodic tasks and an intelligence layer for autonomous and adaptive control behavior. In this research work, a rule-based system is used as the artificial intelligence component, where we propose to optimize the inference process by splitting the rule base into two sub-bases; the effective one and the general meta base. With this facility, the coordination in decision making for the distributed platform and system Quality of Service (QoS) in complex and robust system implementation should be faced by new strategies and correct policies. In this sense, we propose a new protocol for the coordination in the two levels of the reconfiguration process to get correctness in the system results. Dealing with performance, we propose to supervise the intelligence QoS of the whole distributed system. Also, we present the correctness of the coordination between the different decisions by the field of the coordination factor. An implementation of the paper contribution with Drools as an integrated rule-based system framework to RTDroid and a discussion of the inferring time and the memory consumption are presented in this paper. INDEX TERMS Real-time embedded control systems, intelligence reconfiguration, rule-based system, intelligence QoS, coordination factor, intelligence impact.

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Intelligent distributed control systems

Cited by 19 publications

References 20 publications

Agent-based middleware architecture for reconfigurable manufacturing systems

Agent-based middleware architecture for reconfigurable manufacturing systems

Investigation of Unmeasured Parameters Estimation for Distributed Control Systems

Dynamic Reconfiguration of Intelligence for High Behaviour Adaptability of Autonomous Distributed Discrete-Event Systems

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