Abstract
Educational robotics has had an increasing growth in the past years, mainly in teaching Science, Technology, Engineering, Arts and Mathematics (STEAM). These robotics-based learning methods have since gone from home to be used every day in school learning activities. There still is, however, a big moat from the available resources and the effective use of these tools by teachers in K-12 schools. This study aims to gather in a single location a dataset of most available educational robotic platforms and related learning materials. The goal is to have this knowledge open, freely accessible and editable by manufactures and learning resources providers, helping to increase the adoption of educational robotics in STEAM education. © 2018 IEEE.

Abstract
The gravitational search algorithm (GSA) is reviewed, by presenting a tutorial analysis of its key issues. As any other metaheuristic, GSA requires the selection of some heuristic parameters. One parameter which is crucial in regulating the exploratory capabilities of this algorithm is the gravitational constant. An analysis regarding this parameter selection is presented and a heuristic rule proposed for this purpose. The GSA performance is compared both with a hybridization with particle swarm optimization (PSO) and standard PSO. Preliminary simulation results are presented considering simple continuous functions optimization examples.

Abstract
Half-cycle Posicast Control is currently used in a vast range of applications. Although the proved benefits of this technique, one of its major disadvantages concerns model uncertainties. This has motivated the development and integration of robust methods to overcome this issue. In this paper, a practical experiment for auto-tuning of a two degrees of freedom control configuration using a Half-Cycle Posicast pre-filter (or input-shaping), and a PID controller under parametric variations is presented. The proposed method requires using an oscillatory system model in an auto-tuning control structure. The error derivative among the model and system output is used to trigger both the identification and retuning procedure. The proposed method is flexible for choosing identification plus optimization methods. Practical results obtained for electronic filter plants suggest improved performance for the considered cases. © 2018

Abstract
The proposed tuning method for integrating processes, which is based on Magnitude optimum criterion, has been extended to PID types of controllers. The method requires either the process transfer function (in frequency-domain) or the measurement of process steady-state change (in time-domain). The PID controller parameters are calculated analytically by solving fourth-order polynomial. By changing reference-weighting parameter b, the user can favour tracking (higher b) or control performance (lower b). The proposed method has been tested on several process models (lower-order with delay, higher order with delay, and a phase non-minimum process) and the closed-loop responses were relatively fast and non-oscillatory. The comparison with other tuning method based on process step-response data results in favourable tracking and control performance. © 2018

Abstract
Multidimensional or n-D systems (n>1) are models having several independent variables. Among the topics related with this type of systems, stability has been attracting the interest of many researchers. The extension of the stability theory extension from 1-D systems to high dimensions is not straightforward. In this paper, four known meta-heuristics (MH) are used to study systems stability based on their polynomial characteristics over the variables boundaries. The four MH consist of genetic algorithms, particle swarm optimisation, cuckoo search and differential evolution. The results obtained with these MH are compared and the best algorithm highlighted. The computational experiments demonstrate that MH can be applied in studding multidimensional system stability.

Abstract
This paper evaluates a robust Model Predictive Controller (MPC) based on Sliding Modes (SMPC) for the downstream level control in irrigation canal pools. Its features are compared with the conventional Generalized Predictive Controller (GPC), regarding set point tracking (water level) and output disturbances (offtake discharges). Simulation results suggest feasibility of applying SMPC for gate manipulation, with suitable command signals and robustness.