This paper presents an efficient algorithm to solve the multi-objective (MO) voltage control problem in distribution networks. The proposed algorithm minimizes the following three objectives: voltage variation on pilot buses, reactive power production ratio deviation, and generator voltage deviation. This work leverages two optimization techniques: fuzzy logic to find the optimum value of the reactive power of the distributed generation (DG) and Pareto optimization to find the optimal value of the pilot bus voltage so that this produces lower losses under the constraints that the voltage remains within established limits. Variable loads and DGs are taken into account in this paper. The algorithm is tested on an IEEE 13-node test feeder and the results show the effectiveness of the proposed model.

The objective of the present study is to compare three control approaches: ON/OFF control, fuzzy logic, and convolutional neural networks (CNN) implemented in Python for controlling the real-time trajectory tracking of a six-axis industrial robotic arm. This analysis has significant applications in fields that require a high level of precision, such as automated welding and surgical interventions in the medical domain. To evaluate the performance and adaptability of the control models, we will analyze the results using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), as well as metrics including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Jaccard Index, and Pearson's correlation coefficient. The results obtained reveal valuable information about the advantages and limitations of each control approach, highlighting the effectiveness of CNNs in visual perception and trajectory tracking. The ability of CNNs to interpret visual complexities is presented as a key factor for their success in industrial robotics and automation applications, suggesting a promising future for these technologies in dynamic environments.

This paper shows the implementation and performance analysis of motion control algorithms for a 3D Concrete Printing system based on an industrial robotic arm. This work is part of the project of digital fabrication of low-cost housing. Regarding the technological architecture integrated in the present work, the hardware devices used are the EPSON C12 industrial robot arm and the RC700-A controller, and the software tools are Fusion 360 and RC+. To evaluate the point extraction and sequencing algorithms of a 3D structure and the motion control algorithms of the robot arm, three test wall models were designed: a semi-circle with horizontal undulations, a semi-circle with vertical undulations and an orthohedron without undulations. For the performance analysis, 140 trajectory times were extracted for each test model. The extracted values are the trajectory time intervals of the wall model envelope for each layer and of the internal trajectory of the wall model infill. Due to the increasing and decreasing trends of time for specific cases, it was concluded that in the working area of the robot arm, there are parts in which the robot is more efficient and therefore the joints offer less inertia for certain types of movements, for example straight or curved movements and short or long movements.

The manufacturing, assembly, and packaging processes of industrial hinges must improve their productivity. To address this challenge, a technological system based on a robot arm and vision machine was developed, integrated, and evaluated. The devices that make up the hardware architecture are the following: the Epson VT6L six-degrees-of-freedom industrial robot arm, the Compact Vision CV2-HA image-processing device, the Basler acA1600-20gc camera, and the end effector based on 3 kg holding force electromagnets. The software elements used for the development of this automated system are the SPEL+ programming language and VisionGuide tool, both of which are integrated into the Epson RC+ robotic system development environment. For the performance evaluation of the hinges and packaging identification algorithms, 80 hinges of each type were used as input to the robotic system workstation. As a result, the following results were obtained: (1) the hinge identification algorithms were 100% correct, (2) the primary packaging identification algorithm was 100% correct, and (3) the hinge assembly algorithm was 92.5% correct because it had six errors. These results provide evidence of the effectiveness of the developed system. In addition, the motion times of the robot arm were analyzed in detail to identify opportunities for improving the automated production process.

En este artículo se propone la metodología para realizar la partición eléctrica de una red de distribución utilizando algoritmos de agrupamiento de datos como K-means y DBSCAN. Los datos se obtienen generando variaciones en los parámetros de la red y simulando el perfil de voltaje con el software OpenDSS. La metodología propuesta se implementa en redes de distribución estándar de prueba IEEE de 34 y 123 barras. Los resultados obtenidos son comparados con métodos obtenidos de la literatura.