The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction of as much energy as possible from PV panels, and they require algorithms to extract the Maximum Power Point (MPP). Several intelligent algorithms show acceptable performance; however, few consider using Artificial Neural Networks (ANN). These have the advantage of giving a fast and accurate tracking of the MPP. The controller effectiveness depends on the algorithm used in the hidden layer and how well the neural network has been trained. Articles over the last six years were studied. A review of different papers, reports, and other documents using ANN for MPPT control is presented. The algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm. ANN MPPT algorithms deliver an average performance of 98% in uniform conditions, exhibit a faster convergence speed, and have fewer oscillations around the MPP, according to this research.

This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.

In this paper, a two-degrees-of-freedom controller is proposed for robust speed regulation in permanent magnet synchronous motors (PMSM). Our proposal constitutes an extension of a control scheme previously introduced in the literature for permanent magnet brushed dc motors to the case of PMSM. We formally prove global exponential stability of the desired equilibrium point, and we take into account the nonlinear motor electric dynamics during the stability proof. Several experiments are presented, which verify that the closed-loop control system is robust with respect to parameter uncertainties and outstanding torque disturbance rejection is performed. These results are compared to those obtained with a standard proportional-integral speed control scheme. We conclude that good performance, simplicity, and easy controller commissioning in practice are significant advantages of our proposal, which render it suitable for the motor drive community.

Tool condition monitoring systems are essential in micromilling applications. A tool's slenderness requires high-precision monitoring systems for online measurements. In most cases, tool health is indirectly estimated by processing and analyzing the cutting process parameters. In that sense, the main challenge lies in the proper selection of the process parameters and their processing techniques, so that a robust and accurate assessment of the tool's health is obtained. This paper proposes a frequencyand time-frequency-based analysis of cutting force and vibration signals for estimating the tool condition of a high-speed micromilling process. Measurements obtained from different cutting conditions were utilized in the analysis. The results indicate variations in the dominant frequencies, which result from tool wear. Furthermore, it is important to note that the analysis results obtained from the two process signals provide more reliable results and improve the sensing bandwidth.

PID controllers are widely used and adaptable to various types of systems. However, for the response to be adequate under different conditions, the PID gains must be adjusted. The tuning is made according to the difference between the reference value and the real value (error). This work presents a self-adjusting PID controller based on a backpropagation artificial neural network. The network calculates the appropriate gains according to the desired output, that is, the dynamic response desired which is composed of the transient part and the stationary part of the step response of a system. The contribution of the work is that in addition to using the error for network training, the maximum desired values of overshoots, settling times, and stationary errors were used as input data for the network. An offline training database was created using genetic algorithms to obtain the dynamic response data associated with PID gains. The genetic algorithm allows getting data in different operating ranges and allows using only stable gains combinations. The database was used for training. Subsequently, the neural network estimates an appropriate gain combination, adapting to the error and the desired response. The method performance is evaluated by controlling the speed of a direct current motor. The results indicate an average error of 4% for the database between the requested and system response. On the other hand, the gains estimated by the network in the test dataset (1544 combinations) did not cause instability and complying with the expected dynamic response in 86% of the dataset.

This paper presents a robot prototype for an undergraduate laboratory program designed to fulfill the criteria laid out by ABET. The main objective of the program is for students to learn some basic concepts of embedded systems and robotics, and apply them in practice. For that purpose, various practical laboratory exercises were prepared to teach different aspects of communications, control, mechatronics, and microcontrollers. The practicals are organized such that the students can systematically solve real-world problems. The most important feature of the presented program is that, it incorporates interdisciplinary knowledge, and inculcates technical and professional skills required in pursuing a successful career. Furthermore, students and instructors can modify the software and hardware units of the robot prototype as necessary, to explore more ideas and to apply the robot in other mechatronics-related courses. A digital electronics course taught at the Automation Department at Universidad Autónoma de Querétaro, Querétaro, Mexico, is presented as a case study in which the evaluation process was based on ABET criteria and the corresponding student outcomes. A student survey elicited students' observations of, and interest in, the learning process. The positive student feedback and student academic outcomes indicate that the inclusion of prototype had a significant impact on student academic outcomes.