Abstract
The refinement of an existing internet multimedia tool to support teaching/learning activities concerning GRAFCET is reported here. The tool called c-GRAFCET resulted in an updated and improved version called e-GRAFCET+. The overall objective of this internet-based tool is to provide fundamental aspects regarding GRAFCET presented with animated examples, promoting students self-learning. While originally this tool was developed to be used by Portuguese speakers, now it is also available in English. The tool upgrading resulted in the elaboration of a new internet portal, incorporating new features, concerning the site management and question quizzes updating. A very important feature of e-GRACFCET+, is to be prepared to work on most devices regardless of operating systems and screen sins, such as smart-phones and tablets.

Abstract
The coefficient diagram method (CDM) is one of the easiest methods for model based control system design. Its core is based on an algebraic method but it also encompasses a graphical analysis diagram that helps the user to evaluate the three main closed-loop system requirements: dynamic behaviour, robustness and stability. This later characteristic is analysed by a set of stability conditions derived from the previous work of Lipatov and Sokolov on sufficient conditions for stability. However, in CDM, only a fraction of the total conditions are considered. This work will show that this fact increases the inconclusive area within the stability space. Moreover an extended set of CDM stability conditions, in conjunction with its graphical interpretation, will be presented.

Abstract
The performance of multi-objective evolutionary algorithms can severely deteriorate when applied to problems with 4 or more objectives, called many-objective problems. For Pareto dominance based techniques, available information about some optimal solutions can be used to improve their performance. This is the case of corner solutions. This work considers the behaviour of three multi-objective algorithms [Non-dominated sorting genetic algorithm (NSGA-II), Speed-constrained multi-objective particle swarm optimization (SMPSO) and generalized differential evolution (GDE3)] when corner solutions are inserted into the population at different evolutionary stages. The problem of finding corner solutions is addressed by proposing a new algorithm based in multi-objective particle swarm optimization (MOPSO). Results concerning the behaviour of the aforementioned algorithms in five benchmark problems (DTLZ1-5) and respective analysis are presented.

Abstract
Proportional, integral and derivative controller tuning can be a complex problem. There are a significant number of tuning methods for this type of controllers. However, most of these methods are based on a single performance criterion, providing a unique solution representing a certain controller parameters combination. Thus, a broader perspective considering other possible optimal or near optimal solutions regarding alternative or complementary design criteria is not obtained. Tuning PID controllers is addressed in this paper as a many-objective optimization problem. A Multi-Objective Particle Swarm Optimization algorithm is deployed to tune PID controllers considering five design criteria optimized at the same time. Simulation results are presented for a set of four well known plants.

Abstract
Multidimensional systems, or n-D systems, are systems having several independent variables. Several topics, in particular stability, of n-D systems (n > 1) have attracted the interest of many researchers. The main reason, is because the extension stability theory of 1-D systems to systems with higher dimensions is not straightforward. In this paper, two adopted meta-heuristics algorithms are used for complementing the study of systems stability based on their polynomial characteristics over the variables boundaries. The two meta-heuristics are genetic algorithm and particle swarm optimization due to its popularity. Practical results of both meta-heuristics are compared and the better algorithm highlighted. The results demonstrate that meta-heuristics can be applied in studding multidimensional system stability.

Abstract
This work presents a new approach to tune the parameters of the discontinuous component of the Sliding Mode Generalized Predictive Controller (SMGPC) subject to constraints. The strategy employs Particle Swarm Optimization (PSO) to minimize a second aggregated cost function. The continuous component is obtained by the standard procedure, by Sequential Quadratic Programming (SQP), thus yielding a dual optimization scheme. Simulations and performance indexes for a non minimum linear model result in a better performance, improving robustness and tracking accuracy.