Abstract

Abstract

Abstract
This work addresses the problem of finding the best controller parameters in order to improve the response of a single degree-of-freedom structural system under earthquake excitation. The control paradigm considered is based on brain emotional learning (BEL) and the actuation over the building dynamics is carried out by changing the stiffness of a magneto-rheological damper. A typical BEL-based controller requires the definition of several parameters which can prove difficult and non-intuitive to obtain. For this reason, an evolutionary-based search technique has been added to the current problem framework in order to automate the controller design. In particular, the particle swarm optimization method is chosen as the evolutionary based technique to be integrated within the current control paradigm. The obtained results suggest that, indeed, it is possible to parametrize a BEL controller using an evolutionary-based algorithm. Moreover, a simulation shows that the obtained results can outperform the ones obtained by manual tuning each controller parameter individually.

Abstract

Abstract
Power systems of most EU countries will face substantial changes driven mainly by the EU Clean Energy Package and the expected increasing role of customers making use of demand response and distributed self-generation and self-storage resources (self-DG&S). This work aims to analyze the impact of these two factors on the long term evolution of both utility scale centralized generation and storage (CG&S), and self-DG&S. An hourly resolution generation expansion model based on cost minimization for CG&S and expenses' minimization for self-DG&S is used. Four case studies covering the period 2018-2047 for a real sized system (the Spanish one) are presented. Results highlight a strong synergy between battery costs and solar photovoltaic investments, while wind power seems to be better complemented by peaker thermal units.

Abstract
Although the everlasting goal of achieving a zero-defect production system is not new, the current state of the art has not yet allowed to reach this objective. This paper describes the architectural system foundations of the European R&D GO0DMAN project where a multi-agent system is developed as the mean to provide a real-time, multi-level and multi-stage approach to reach a zero-defect production system in multi-stage environments. Particularly, the paper focuses the integration of software agents and quality control stations forming smart inspection tools, aligned with the cyber-physical systems principles. The proposed approach is validated using an electrical motor testbed, where the agent-based system allows the digitization of the running testing system in a multi-stage schema.