Intelligent distributed and supervised flow control methodology for production systems

Abstract

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.

Hierarchical control of production flow based on capacity allocation for real-time scheduling of manufacturing systems

“…The research reported in this paper is outgrowth of our previous work published in [19], in which the continuous production-flow control methodology of two levels has been developed with a set of distributed surplus-based controllers at the lower level and a supervisory controller at the higher level. The objective is to adjust the machine's production rates in such a way that satisfy the demand while maintaining the overall performances within acceptable limits.…”

“…Section 2 describes, firstly, the distributed and supervised fuzzy control methodology, originally developed in [19], and secondly, the sampling and dispatching procedure. Section 3 presents the scenario and experimental results for re-entrant and multiple-part-type real manufacturing cell taken from [16] for comparison purposes.…”

“…Indeed, at the control level, the capacity allocation problem is approached from the control theory viewpoint [4][8] [19] [22]. The problem is considered as a regulation of flow of parts by adjusting the machine's processing rates.…”

“…Particularly, in scheduling problems, the objective is to find a way to assign and sequence the use of shared resources such that production constraints are satisfied. Although traditionally addressed by: management science, operation research and industrial engineering, its complexity and importance have concentrated the efforts of different research communities, concerned with artificial intelligence [10], dynamic programming, system simulation [9] [11] and control theory [4][8] [19] [22].…”

Artificial cognitive control system based on the shared circuits model of sociocognitive capacities. A first approach

“…The nonlinearities at the control problem are present at MSCM control, but the neural block control uses a specific mathematical model developed to control a synchronous generator. The intelligent distributed and supervised flow control methodology for production systems presented by Tamani et al (2009) describes distributed control architecture. As MSCM, it takes inherent advantages such as modularity, reconfigurability, adaptability, fault tolerance, extensibility, etc.…”