This article presents a 2D pose estimation method for an omnidirectional mobile robot with Mecanum wheels, using an extended Kalman filter (EKF) formulated on the Lie group $\mathrm{SO}\left(2\right)$. The purpose is estimate the robot’s position and orientation by fusing angular velocity measurements from the wheel encoders with data from an IMU. Employing Lie algebra, the EKF provides a consistent and compact representation of rotational motion, improving prediction and update steps. The filter was implemented in ROS 1 and validated in simulation using Gazebo, with a reference trajectory and real measurements used for evaluation. The system delivers higher pose estimation precision, validating the effectiveness in rotational maneuvers.

This paper addresses the need for accessible and interoperable supervision solutions within the Industry 4.0 paradigm, particularly for small-scale or resource-constrained environments. The proposed system integrates a web-based architecture using opensource technologies to enable real-time industrial monitoring and data acquisition. A hybrid setup was developed, combining a virtual glass manufacturing process in Factory IO with a physical three-phase induction motor controlled by a Modicon M580 PLC. The system architecture includes a local HMI developed in Control Expert and a remote interface built with React and Node.js, both synchronized through a MySQL 8.0 database populated via Python 3.13 using the Modbus TCP/IP protocol. Experimental results demonstrate consistent data synchronization, reliable multi-platform integration, and an average end-to-end latency of 156 ms, validating the feasibility of the approach for IIoTbased applications. The solution demonstrates how general-purpose web technologies can be effectively repurposed for industrial use, offering a cost-effective and scalable alternative to traditional SCADA systems. The proposed architecture is easily replicable, adaptable to various process configurations, and suitable for academic, prototyping, and SME environments.

This paper presents the design, development, and testing of a smart home automation system that integrates a Siemens LOGO! programmable logic controller (PLC) with an ESP32 microcontroller to enable dual-mode control—manual and voice-activated. The system automates essential home functions such as lighting, irrigation, gate control, and ventilation. Through the use of the fauxmoESP library, the ESP32 communicates with Amazon Alexa, converting voice commands into GPIO signals interpreted by the PLC. Manual control is retained via pushbuttons, ensuring operational redundancy in case of network or hardware failure. The system architecture includes optocouplers and relays to ensure voltage compatibility and device protection between the 3.3 V microcontroller and the 12–24 V PLC inputs. Functional tests revealed a 100% success rate in manual operations and over 95% in voice-controlled actions, with notable differences in response times. A cost breakdown and risk analysis are also included to assess feasibility and sustainability. This prototype highlights a practical, low-cost solution for residential automation, with scalability potential for broader smart home applications and educational or industrial implementations.

This paper presents a comparative experimental evaluation of three control strategies for angular position regulation of a Vertical Take-Off and Landing (VTOL) system: a conventional Proportional–Integral–Derivative (PID) controller, a Linear Quadratic Integral (LQI) controller, and a proposed nonlinear PID (NL-PID) controller. The proposed strategy extends the conventional PID structure by introducing a nonlinear gain that dynamically modulates the control action according to the evolution of the tracking error. The controller gains of the nonlinear PID are obtained through a genetic algorithm optimization process based on the Integral of the Squared Error (ISE) and maximum overshoot. Experimental evaluation is performed through three tests under different operating conditions: a normal test, an uncertain physical parameter test, and an external perturbation test. The angular position response and control effort of the three controllers are analyzed, with tracking performance quantitatively evaluated using the Integral of the Squared Error (ISE), Integral of the Absolute Error (IAE), and percentage overshoot (PO). The results show that the proposed NL-PID consistently provides the best tracking performance among the evaluated controllers. In the normal test, the NL-PID achieves the lowest ISE and IAE, with reductions in ISE of 16.34% and 7.25% compared with the conventional PID and LQI controllers, respectively. Under an uncertain physical parameter, these reductions increase to 19.89% and 7.54%, respectively, while the NL-PID also exhibits the lowest IAE and percentage overshoot. Under an external perturbation, the NL-PID achieves the lowest ISE and IAE, reducing the accumulated squared and absolute tracking errors by 12.06% and 15.65% compared with the PID controller, and by 22.50% and 35.29% compared with the LQI controller, respectively. These results demonstrate the potential of the proposed nonlinear PID to improve angular position tracking performance under different operating conditions while preserving the simplicity of the conventional PID structure.

Solar and wind variability can limit renewable generation under contrasting regional conditions. This study evaluates a solar–wind–biomass hybrid system for Puerto Quito, Valencia, and Tena, Ecuador, using MATLAB/Simulink R2024b with NASA POWER meteorological inputs, PVGIS as a complementary solar reference, and biomass assumptions informed by the Ecuadorian Bioenergy Atlas. Under a controlled biomass scenario of 1500 t/year, the biomass subsystem supplied 87.7 kW. Mean hybrid outputs were 137, 130, and 128 kW, equivalent to 1200.1, 1138.8, and 1121.3 MWh/year, respectively. Adding the common biomass contribution reduced the relative regional spread from 22.3% to 7.0%, while the absolute Puerto Quito–Tena difference remained 9 kW. Biomass sensitivity and analytical uncertainty screening further characterized the influence of resource and model assumptions on the comparative results.