Abstract
The use of robotic palletizing systems has been increasing in the so-called Fast Moving Consumer Goods (FMCG) industry. However, because of the type of solutions developed, focused on high performance and efficiency, the degree of adaptability of packaging solutions from one type of product to another is extremely low. This is a relevant problem, since companies are changing their production processes from low variety/high volume to high variety/low volume. This environment requires companies to perform the setup of their robots more frequently, which has been leading to the need to use offline programming tools that decrease the required robot stop time. This work addresses these problems and, in this paper, is described the solution proposed for the automated offline development of collision free robot programs for palletizing applications. © Springer International Publishing AG 2018.

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

Abstract
This paper addresses the problem of finding the best Brain Emotional Learning (BEL) controller parameters in order to improve the response of a single degree-of-freedom (SDOF) structural system under an earthquake excitation. The control paradigm considered is based on a semi-active system to control the dynamics of a lumped mass-damper-spring model, being carried out by changing the damping force of a magneto-rheological (MR) damper. A typical BEL based controller requires the definition of several parameters which can be proved 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 (PSO) method was 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, simulation shows that the obtained results can outperform the ones obtained by manual tuning each controller parameter individually.

Abstract
In this paper it is described a hardware experimental setup applied to study the response of structures due to an earthquake excitation. The described hardware experimental setup was applied in the performance and effectiveness evaluation of passive and semi-active vibration control strategies, in order to reduce the structural response due to an earthquake excitation. This study is also meant to validate the accurateness of the developed numerical models that simulate the experimental results. The hardware implementation is described, being the structural response real data and its numerical models compared.