In the last decade, model predictive control (MPC) has been widely studied in power converters, such as voltage source inverters (VSIs). Unfortunately, MPC often presents a high computational burden that limits their applicability, especially when driving multilevel inverters (MLIs) because of their higher number of switching combinations than two-level inverters. As a result, some strategies have been developed to reduce the computational complexity of MPC. One of the most relevant is the use of artificial neural networks (ANNs) to approximate the behavior of an MPC. However, ANNs require to be evaluated at bounded inputs. Otherwise, their response cannot be guaranteed to be a good approximation of the controller they learned from. Furthermore, when driving an LC-filtered VSI, the inductor current can present high peaks due to the cross-coupling effect between the inductor and the capacitor. These current peaks can cause physical damages and loss of performance of an ANN-emulated MPC. This paper presents a new constrained modulated MPC (M ² PC), better suited for ANN emulation, to overcome these issues. The proposed strategy evaluates the cost function once per switching region, allowing easy and intuitive constraint inclusion. Additionally, an overmodulation stage is used to handle negative duty cycles and enhance disturbance rejection. Finally, the proposal is validated through simulations, using MATLAB-Simulink, taking into account different load conditions. Simulations show that the constrained M ² PC keeps the inductor current below its desired limit while having a good performance (low harmonic distortions and fast dynamics) even when the inverter operates near its boundaries.

The main benefits of fuzzy logic control (FLC) allow a qualitative knowledge of the desired system’s behavior to be included as IF-THEN linguistic rules for the control of dynamical systems where either an analytic model is not available or is too complex due, for instance, to the presence of nonlinear terms. The computational structure requires the definition of the FLC parameters namely, membership functions (MF) and a rule base (RB) defining the desired control policy. However, the optimization of the FLC parameters is generally carried out by means of a trial and error procedure or, more recently by using metaheuristic nature-inspired algorithms, for instance, particle swarm optimization, genetic algorithms, ant colony optimization, cuckoo search, etc. In this regard, the cuckoo search (CS) algorithm as one of the most promising and relatively recent developed nature-inspired algorithms, has been used to optimize FLC parameters in a limited variety of applications to determine the optimum FLC parameters of only the MF but not to the RB, as an extensive search in the literature has shown. In this paper, an optimization procedure based on the CS algorithm is presented to optimize all the parameters of the FLC, including the RB, and it is applied to a nonlinear magnetic levitation system. Comparative simulation results are provided to validate the features improvement of such an approach which can be extended to other FLC based control systems.

The growing energy demand around the world has increased the usage of renewable energy sources (RES) such as photovoltaic and wind energies. The combination of traditional power systems and RESs has generated diverse problems due especially to the stochastic nature of RESs. Microgrids (MG) arise to address these types of problems and to increase the penetration of RES to the utility network. A microgrid includes an energy management system (EMS) to operate its components and energy sources efficiently. The objectives pursued by the EMS are usually economically related to minimizing the operating costs of the MG or maximizing its income. However, due to new regulations of the network operators, a new objective related to the minimization of power peaks and fluctuations in the power profile exchanged with the utility network has taken great interest in recent years. In this regard, EMSs based on off-line trained fuzzy logic control (FLC) have been proposed as an alternative approach to those based on on-line optimization mixed-integer linear (or nonlinear) programming to reduce computational efforts. However, the procedure to adjust the FLC parameters has been barely addressed. This parameter adjustment is an optimization problem itself that can be formulated in terms of a cost/objective function and is susceptible to being solved by metaheuristic nature-inspired algorithms. In particular, this paper evaluates a methodology for adjusting the FLC parameters of the EMS of a residential microgrid that aims to minimize the power peaks and fluctuations on the power profile exchanged with the utility network through two nature-inspired algorithms, namely particle swarm optimization and differential evolution. The methodology is based on the definition of a cost function to be optimized. Numerical simulations on a specific microgrid example are presented to compare and evaluate the performances of these algorithms, also including a comparison with other ones addressed in previous works such as the Cuckoo search approach. These simulations are further used to extract useful conclusions for the FLC parameters adjustment for off-line-trained EMS based designs.

Currently, there are limitations in the human–machine interfaces (HMIs) used in industry, either due to the characteristics of users’ cognitive abilities or interfaces, which hinder communication and interaction between humans and equipment. For this reason, this work presents an alternative interaction model based on a voice assistant, Alexa, which promotes more natural, intuitive, direct, and understandable communication. The purpose of this work is the development of an industrial safety system for a controlled electric motor based on Alexa voice assistant, which allows the monitoring of its operating parameters, such as phase current, housing temperature, and rotor vibration, as well as making it possible to control ignition and shut down and change the rotation of the motor with a prior password, as a safety measure. Commercial smart devices and Arduino-compatible modules were used to achieve this, providing them with the Internet of Things (IoT) feature. In addition, several software platforms, such as Blynk, Tuya Smart, Node Red, and Voiceflow, are used to perform data transmission, device management, and programming of the Alexa skill, oriented to the execution of the security and run system. This shows the potential capacity of voice assistants in the industry to deliver information more naturally to humans and obtain optimal notifications. However, problems were evidenced, such as the influence of noise in the environment when communicating with the assistant, the vocalization of words, low voice tones, and accents typical of the language, that will increase the security level of the system and prevent potential identity theft.

Se presenta un resumen de las experiencias obtenidas de la implantación de un sistema de transporte basado en autobuses con locomoción eléctrica-trolebus en el Distrito Metropolitano de Quito, capital de la Re- pública del Ecuador. Además se provee de un análisis del empleo de la electricidad como medio energético para alimentar vehículos urbanos como propuesta de solución para mejorar la movilidad urbana y hacerla sostenible.