Abstract
This paper presents the first phase of the redevelopment of the Electric Vehicle Scenario Simulator (EVeSSi) tool. A new methodology to generate traffic demand scenarios for the Simulation of Urban MObility (SUMO) tool for urban traffic simulation is described. This methodology is based on a Portugal census database to generate a synthetic population for a given area under study. A realistic case study of a Portuguese city, Vila Real, is assessed. For this area the road network was created along with a synthetic population and public transport. The traffic results were obtained and an electric buses fleet was evaluated assuming that the actual fleet would be replaced in a near future. The energy requirements to charge the electric fleet overnight were estimated in order to evaluate the impacts that it would cause in the local electricity network.

Abstract
Nature and biologically inspired metaheuristics can be powerful tools to design PID controllers. The grey wolf optimization is one of these promising and interesting metaheuristics, recently introduced. In this study the grey wolf optimization algorithm is proposed to design PID controllers, and the results obtained compared with the ones obtained with gravitational search and particle swarm optimization algorithms. Simulation results obtained with these three bio-inspired metaheuristics applied to a set of benchmark linear plants are presented, considering the design objective of set-point tracking. The results are also compared with two non-iterative PID tuning techniques. © Springer International Publishing Switzerland 2017.

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.