The continuous changes of the market and customer demands have forced modern automation systems to provide stricter Quality of service (QoS) requirements. This work is centered in automation production system flexibility, understood as the ability to shift from one controller configuration to a different one, in the most quick and cost-effective way, without disrupting its normal operation. In the manufacturing field, this allows to deal with non-functional requirements such as assuring control system availability or workload balancing, even in the case of failure of a machine, components, network or controllers. Concretely, this work focuses on flexible applications at production level, using Programmable Logic Controllers (PLCs) as primary controllers. The reconfiguration of the control system is not always possible as it depends on the process state. Thus, an analysis of the system state is necessary to make a decision. In this sense, architectures based on industrial Multi Agent Systems (MAS) have been used to provide this support at runtime. Additionally, the introduction of these mechanisms makes the design and the implementation of the control system more complex. This work aims at supporting the design and development of such flexible automation production systems, through the proposed model-based framework. The framework consists of a set of tools that, based on models, automate the generation of control code extensions that add flexibility to the automation production system, according to industry 4.0 paradigm.

The paradigm of the fourth industrial revolution poses the combined use of technologies that provide companies with adaptability to face uncertain market scenarios. Different institutions have presented their reference architectures to apply principles of the fourth industrial revolution in a standardized way, being the German proposal Industry 4.0 (I4.0) the most widespread. Nowadays most companies are still in the computerization and digitalization phases prior to being able to connect to I4.0. This situation highlights the need for solutions for the integration of physical assets in I4.0. However, most of the work in this area focuses on ad-hoc solutions, or at least technology-dependent ones. This paper presents a multi-layer approach for integrating physical assets in I4.0. The goal is to separate the different concerns related to the integration of physical assets into abstract layers. This approach allows addressing these concerns in a generic way, regardless of the type of asset and/or the technology used, as well as efficient, since it addresses each concern separately. This approach is complemented by a methodology that provides guidelines for integrating physical assets according to the multi-layer approach. The applicability of the multi-layer approach and the integration methodology is put to the test by integrating a robotic arm.

The I4.0 component is a key concept in RAMI 4.0 reference architecture that can be understood as a Cyber Physical Systems, where the asset corresponds to the physical part and the Asset Administration Shell (AAS) corresponds to the cyber part. The implementation of the AAS has received an increasing attention in the last few years. Industrial agents have become a common solution for this issue because their features fulfil several of the requirements for the development of AASs. However, the lack of design methodologies and design development tools can explain the little adoption of these systems in industry. In this context, this work contributes a pattern for the implementation of AASs based on industrial agents from which agent templates for different applications can be derived. In order to assess the applicability of the proposed pattern, it is applied in the implementation of AAS for manufacturing resources as well as for the logical representation of product assets, covering the production phase of the I4.0 component.

Context-aware applications present common requirements (e.g., heterogeneity, scalability, adaptability, availability) in a variety of domains (e.g., healthcare, natural disaster prevention, smart factories). Besides, they do also present domain specific requirements, among which the application concept itself is included. Therefore, a platform in charge of managing their execution must be generic enough to cover common requirements, but it must also be adaptable enough to consider the domain aspects to meet the demands at application-level. Several approaches in the literature tackle some of these demands, but not all of them, and without considering the applications concept and the customization demands in different domains. This work proposes a generic and customizable management architecture that covers both types of requirements based on multi-agent technology and model-driven development. Multi-agent technology is used to enable the distributed intelligence needed to address many common requirements, whereas model-driven development allows to address domain specific particularities. On top of that, a customization methodology to develop specific platforms from this generic architecture is also presented. This methodology is assessed by means of a case study in the domain of eHealthCare. Finally, the performance of MAS-RECON is compared with the most popular tool for the orchestration of containerized applications.

El paradigma de la Industria 4.0 plantea el uso de herramientas y tecnologías que aumenten la eficiencia y la capacidad de adaptación de los procesos productivos para hacer frente a un mercado globalizado y con una alta competitividad. El concepto de Gemelo Digital es clave en el contexto de la Industria 4.0, y ha sido objeto de interés para la industria y la academia. Sin embargo, no hay consenso en la definición de este concepto, existiendo principalmente tres interpretaciones: la primera define un Gemelo Digital como un modelo de simulación de alta precisión; la segunda lo interpreta como una representación virtual del estado y el comportamiento de un activo de fabricación para su integración en la Industria 4.0; y la tercera, de reciente aparición, plantea la combinación de las anteriores. En este trabajo, se propone una metodología para la implementación de Gemelos Digitales que se ajusten a esta tercera interpretación. De esta forma, se proponen acciones concretas para la implementación de un Gemelo Digital que tenga capacidad de simular una estación y realizar su puesta en marcha virtual, además de facilitar su integración en la Industria 4.0.

Digital platforms that manage manufacturing applications are complex systems that must provide flexibility to support the execution and reconfiguration of manufacturing plans. The use of modeling approaches may help dealing with this complexity, but the information models should be based on industrial standards to enable their applicability to different manufacturing scenarios. This work contributes a modeling approach towards the definition of information models for achieving manufacturing flexibility in accordance with the reference architecture RAMI 4.0. To that end, this work analyses the reference models that made up RAMI 4.0 to align their requirements with those necessary to support manufacturing flexibility. As a proof of concept, this work illustrates the application of the proposed models in an agent-based platform.