This paper deals with the development of an intelligent distributed and supervised control approach for highvolume production systems in which the flow of parts can be approximated by a continuous (fluid) model. The proposed approach is based on the decomposition of the production system into elementary modules in order to reduce the control design computational complexity. In this context, a two levels control structure is proposed. At the local level, a surplus-based principle is adopted to regulate the production flow for each module according to the distributed structure. The proposed control methodology decides how to adjust the production rate in order to avoid system overloading and eliminate machine starvation or blocking. In this context, the local control law is synthesized by using the Takagi-Sugeno fuzzy systems. At the high level, a supervisory controller is designed to improve the overall system performances. A supervisor provides an additive component for each local controller when the overall system performances deviate from their acceptable domains (degraded mode). This is done by combining both local and global information into a unified formalism by using aggregation operators and according to fuzzy interval representation of the desired objectives. Finally, the feasibility of the proposed methodology is validated with simulation examples.
International audienceThis paper considers the design and the practical implementation of a stable multiple objective real-time scheduling problem for a complex production system. In this paper, a complex production system is viewed as a kind of systems producing a variety of products (multiple-part-type) under constraints and multiple production objectives often conflicting. Previously, fuzzy control theory and fuzzy intervals arithmetic have been used to develop a distributed and supervised continuous-flow control architecture. In this framework, the objective of the distributed control structure is to balance the production process by adjusting the continuous production rates of the machines on the basis of the average local behaviour. The supervisory control methodology aims at maintaining the overall performances within acceptable limits. In the new proposed approach, the problem of a stable real-time scheduling of jobs is considered at the shop-floor level. In this context, as the stability of the control structure is ensured, the actual dispatching times are determined from the continuous production rates through a discretisation procedure. To deal with conflicts between jobs at a shared machine, a decision is made. It concerns the actual part to be processed and uses some criterions representing a measure of the job's priority. The simulation results show the validity of the proposed approach in terms of production cost, robustness and system stability
The control of manufacturing systems is a problem due to its complexity and conflicting goals within the different production objective. In this article, in order to cope with some of these difficulties, we introduce a two-level fuzzy logicbased control structure, allowing the division of the complex control problem into elementary production modules. The proposed approach is characterized by two hierarchical levels. On the lower level of the hierarchy, each production module is designed as an adaptive fuzzy controller which acts independently from the others to regulate the flow of the material into a system, and on the upper level, a supervisor adjusts the parameters of the sub-controllers in order to improve the overall performance and restrict the system to the admissible domain. This leads to hierarchical and distributed control structure.
In this paper, a fuzzy supervisory control technique for manufacturing systems is proposed. The developed method uses a hierarchical structure consisting of a supervisor at the higher level and sub-controllers at the lower one. Each sub-controller regulates the production flow at each production resource in order to: reduce the difference between the cumulative production and demand, avoid overloading, and eliminate machine starvation or blocking. Based on the information about the overall system's performance, the supervisor tunes the predesigned local controllers by adjusting the production capacity of each resource. The feasibility of the proposed method is illustrated by a simulation example for a manufacturing system.
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