The interconnection between sensors, controllers and instruments through a communication network plays a vital role in the performance and effectiveness of a control system. Since its inception in the 90s, the Object Linking and Embedding for Process Control (OPC) protocol has provided open connectivity for monitoring and automation systems. It has been widely used in several environments such as industrial facilities, building and energy automation, engineering education and many others. This paper presents a novel OPC-based architecture to implement automation systems devoted to R&D and educational activities. The proposal is a novel conceptual framework, structured into four functional layers where the diverse components are categorized aiming to foster the systematic design and implementation of automation systems involving OPC communication. Due to the benefits of OPC, the proposed architecture provides features like open connectivity, reliability, scalability, and flexibility. Furthermore, four successful experimental applications of such an architecture, developed at the University of Extremadura (UEX), are reported. These cases are a proof of concept of the ability of this architecture to support interoperability for different domains. Namely, the automation of energy systems like a smart microgrid and photobioreactor facilities, the implementation of a network-accessible industrial laboratory and the development of an educational hardware-in-the-loop platform are described. All cases include a Programmable Logic Controller (PLC) to automate and control the plant behavior, which exchanges operative data (measurements and signals) with a multiplicity of sensors, instruments and supervisory systems under the structure of the novel OPC architecture. Finally, the main conclusions and open research directions are highlighted.

Por razones de sostenibilidad y estrategia energética, entre otras, existe en la actualidad una tendencia clara hacia el uso de nuevas formas de obtención, almacenamiento y gestión de energía, más eficientes y con un carácter eminentemente sostenible. Con este fin, se está investigando sobre sistemas de almacenamiento de energía; de los que uno de los más prometedores, en lo que a capacidad y movilidad se refiere, es el basado en hidrógeno. En el presente trabajo se obtiene un modelo para predecir el comportamiento dinámico de una pila de combustible alimentada por hidrógeno, lo cual permitirá mejorar su control entre otras aplicaciones. Las variables usadas en esta investigación se han extraído de un banco de pruebas real, donde se monitoriza una pila de combustible mientras se producen variaciones en una carga programable conectada a la salida de la misma. Para realizar este modelado se opta por estudiar la implementación de un modelo híbrido basado en técnicas de agrupamiento y, posteriormente, técnicas inteligentes de regresión con redes neuronales artificiales sobre cada uno de los grupos. La propuesta se ha probado con dos conjuntos de datos de validación, consiguiendo resultados altamente satisfactorios.

A fuel cell is a complex system, which produces electricity through an electrochemical reaction. For the formal application of control strategies on a fuel cell, it is very important to have a precise dynamic model of it. In this paper, a dynamic model of a real hydrogen fuel cell is obtained to predict its response. The data used in this paper to obtain the model have been acquired from a real fuel cell subjected to different load patterns by means of a programmable electronic load. Using this data, a nonlinear model based on a hybrid intelligent system is obtained. This hybrid model uses artificial neural networks to predict the output current of the fuel cell in a very precise way. The use of a hybrid scheme improves the performance of neural networks reducing to half the mean squared error obtained for a global model of the fuel cell.

The price to be paid for the great dynamic richness of nonlinear systems is the lack of a unitary theory that allows control-system design to be tacked in a way that is parallel or analogous to that of linear systems. We propose a general methodology that uses fuzzy logic to systematically and formally synthesize nonlinear control systems, which are stable by design. Although this methodology is based on Lyapunov theory, it avoids searching for Lyapunov functions. This allows the synthesis procedure to be systematic as well as formal and, especially, independent of heuristics. Some examples of nonlinear control are solved in this paper, thus allowing assessment of the proposed methodology.