Paúl Ayala

The integration of intelligent voice-control systems represents a critical pathway for enhancing driver comfort and reducing cognitive distraction in modern vehicles. Currently, voice assistants capable of accessing real-time vehicular data (e.g., engine parameters) or controlling actuators (e.g., door locks) remain exclusive to premium brands. While aftermarket solutions like Amazon’s Echo Auto provide multimedia functionality, they lack access to critical vehicle systems. To address this gap, we develop a novel architecture leveraging the OBD-II port to enable voice-controlled telematics and actuation in mass-production vehicles. Our system interfaces with a Toyota Hilux (2020) and Mazda CX-3 SUV (2021), utilizing an MCP2515 CAN controller for engine control unit (ECU) communication, an Arduino Nano for data processing, and an ESP01 Wi-Fi module for cloud transmission. The Blynk IoT platform orchestrates data flow and provides user interfaces, while a Voiceflow-programmed Alexa skill enables natural language commands (e.g., “unlock doors”) via Alexa Auto. Experimental validation confirms the successful real-time monitoring of engine variables (coolant temperature, air–fuel ratio, ignition timing) and secure door-lock control. This work demonstrates that high-end vehicle capabilities—previously restricted to luxury segments—can be effectively implemented in series-production automobiles through standardized OBD-II protocols and IoT integration, establishing a scalable framework for next-generation in-vehicle assistants.

In recent years, the study of model predictive control (MPC) in power electronics has gained significant attention due to its ability to optimize system performance and improve the dynamic control of complex power converters. There are two types of MPC: finite control set (FCS) and continuous control set (CCS). The FCS–MPC has been studied more in regard to these two types of control due to its easy and intuitive implementation. However, FCS–MPC has some drawbacks, such as the exponential growth of the computational burden as the prediction horizon increases and, in some cases, a variable frequency. In contrast, generalized predictive control (GPC), part of CCS–MPC, offers significant advantages. It enables the use of a longer prediction horizon without increasing the computational burden in regard to its implementation, which has practical implications for the efficiency and performance of power converters. This paper presents the design of GPC applied to single-phase multilevel voltage source inverters, highlighting its advantages over FCS–MPC. The controller is optimized offline, significantly reducing the computational cost of implementation. Moreover, the controller is tested in regard to R, RL, and nonlinear loads. Finally, the validation results using a medium-performance controller and a Hardware-in-the-Loop device highlight the improved behavior of the proposed GPC, maintaining a harmonic distortion of less than 1.2% for R and RL loads.

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.

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 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.