Abstract
To satisfy the complex needs of a production process control, many architectures have been proposed which regularly can be classified into hierarchical (e.g. ISA 95 standard), heterarchical (e.g. multi-agent systems) and holonic or semiheterarchical, based on the holarchy notion (self-regulating holons organization with “smart” properties). A manufacturing system under the holonic approach (HMS) is considered part of the Intelligent Manufacturing Systems (IMS) implying that the HMS are defined as highly distributed organizations. The goal of these organizations is to decentralize manufacturing into individual entities called holons, which are autonomous, cooperative and intelligent. A. Koestler defines the concept of Holon as a self-similar or fractal, stable and coherent structure consisting of several holon-shaped sub-structures. Thus, no elements in the holarchy can self-develop without their subordinated parts or components. This characteristic ensures that HMS are stable structures that can resist disturbances. Applying this concept results in control processes that are generalized in terms of the next-generation manufacturing systems. The state of the art reveals that traditional manufacturing systems and HMS approaches are characterized by the Production Unit (PU). Examples of HMS proposed during the last decades with their strengths and weaknesses are summarized hereafter. Finally, the goal of this research is to analyze, design, and validate a holonic PU (HPU) model of intelligent control based on HMS. Primary results show an implementation of a decentralized industrial automation. There are given HPU information models in order to establish a control architecture for manufacturing systems to support real-time plant operation.
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Cruz Salazar, L.A., Rojas Alvarado, O.A., Chacón, E. (2019). Implementing Industrial Control Automation for Holonic Manufacturing Systems Based on the Production Unit Architecture. In: Borangiu, T., Trentesaux, D., Thomas, A., Cavalieri, S. (eds) Service Orientation in Holonic and Multi-Agent Manufacturing. SOHOMA 2018. Studies in Computational Intelligence, vol 803. Springer, Cham. https://doi.org/10.1007/978-3-030-03003-2_18
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