Abstract
This paper describes a laboratory control theory experiment supported by the use of a DCMotor Educational Kit. The impact, as a teaching aid, of the proposed laboratory control experiment is evaluated, having in mind the student’s feedback. The DC motor that is used in the developed educational Kit is the EMG30, being a low cost 12V motor equipped with encoders and a 30:1 reduction gearbox. The experiment is based on real hardware and on simulation, using the SimTwo realistic simulation software. In order to implement the realistic simulation the EMG30model was obtained. Students’ feedback was acquired using a questionnaire and the results confirmed the importance given to these practical experiments. © Springer International Publishing Switzerland 2017.

Abstract
Integration of emerging energy resources in distribution level reveals new opportunities for decision makers to coordinate various energy vectors under the concept of multi-energy system (MES). In this paper, the behavior of a multi-energy player (MEP) who can trade more than one energy carrier to enhance operational flexibility of MES has been investigated. MEP participates in retail and wholesale energy markets to maximize its profit. The strategic behavior of MEP in these two markets is modeled as two synchronized bi-level problems. The problem is linearized and solved through CPLEX 12 solver. Numerical results show the behavior of MEP as a prosumer in the electricity market to make a smother demand and price profile. Moreover, the results reveal a mutual effect of local and wholesale equilibrium prices by increasing the market share of MEP.

Abstract
A summary of contributions made by significant papers from the first 25 years of the Field-Programmable Logic and Applications conference (FPL) is presented. The 27 papers chosen represent those which have most strongly influenced theory and practice in the field.

Abstract
In this paper we propose to model a high pressure gas network using a quadripole approach. Although being simple, the proposed model seems to be adequate to network analysis and control, and is directly extendible to more complex networks, i.e., networks with junctions and loops. The model is proven to be liable on a case study constructed from data supplied by REN Gasodutos, where we investigate the effect of a leakage on the mass flow and pressure along the pipeline with the final objective to define a model based methodology for leakage detection and location. © Springer International Publishing Switzerland 2017.

Abstract
Design and hierarchical control of three phase parallel Voltage Source Inverters are developed in this paper. The control scheme is based on synchronous reference frame and consists of primary and secondary control levels. The primary control consists of the droop control and the virtual output impedance loops. This control level is designed to share the active and reactive power correctly between the connected VSIs in order to avoid the undesired circulating current and overload of the connected VSIs. The secondary control is designed to clear the magnitude and the frequency deviations caused by the primary control. The control structure is validated through dynamics simulations. The obtained results demonstrate the effectiveness of the control structure.

Abstract
Stochastic search algorithms are black-box optimizer of an objective function. They have recently gained a lot of attention in operations research, machine learning and policy search of robot motor skills due to their ease of use and their generality. However, when the task or objective function slightly changes, many stochastic search algorithms require complete re-leaming in order to adapt thesolution to the new objective function or the new context. As such, we consider the contextual stochastic search paradigm. Here, we want to find good parameter vectors for multiple related tasks, where each task is described by a continuous context vector. Hence, the objective function might change slightly for each parameter vector evaluation. In this paper, we investigate a contextual stochastic search algorithm known as Contextual Relative Entropy Policy Search (CREPS), an information-theoretic algorithm that can learn from multiple tasks simultaneously. We show the application of CREPS for simulated robotic tasks.