This paper presents the improvement of an ultrasonic pulse generator for a pipeline inspection gauge (PIG), which uses 64 transducers for inspecting distances up to 100 km with an axial resolution fixed at 3 mm and variable speeds between 0 and 2 m/s. An ultrasonic pulse generator is composed of a high-voltage (HV) MOSFETs, driver logic and an HV power supply. We used a DC-HV DC converter device as the HV power supply because it reduces the size of the ultrasound system considerably. However, pipeline geometry and inspection effects such as hammer and shock cause a variable pulse repetition frequency (PRF), producing voltage drops, poor quality of the HV pulse generated, failures in the dimensioning of defects and damage to devices by over-voltage. Our improvement is to implement a control scheme to maintain the high quality of the HV regardless of the variable PRF. To achieve this, we characterized three transfer functions of the DC-HV DC converter, varying the connected load to 10%, 45% and 80%. For the characterization, we used the least squares technique, considering an autoregressive exogenous (ARX) model. Later, we compared three control schemes: (1) proportional-integral-derivative (PID) tuned by simultaneous optimization of several responses (SOSR), (2) PID tuned by a neural network (NN) and (3) PI tuned by the analytical design method (ADM). The metrics used to compare the control schemes were the recovery time, the maximum over-voltage and the excess energy when the shock and hammer effects happen to occur. Finally, to verify the improvement of the HV pulser, we compared the ultrasonic pulses generated for various frequencies and amplitudes using the pulse generator with and without the control scheme.

Peristaltic pumps represent a fundamental component of hemodialysis machines. They facilitate the transfer of fluids, particularly in the collection and treatment of blood. This study aims to improve pump precision and reliability by reducing steady-state error and optimizing flow consistency, measured in milliliters per minute. A detailed characterization established the relationship between revolutions per minute (RPM) and flow rate (mL/min), with redundant mass and volume measurements supporting accuracy. To model the system’s behavior, two non-linear functions and one linear function were compared, with the polynomial model proving the most accurate and revealing the pump’s inherently non-linear flow behavior. A proportional–integral (PI) controller was then applied, and optimized through step input and non-linear least squares fitting. A key aspect of this study is a comparative validation against a commercial hemodialysis machine, configured identically with the same blood circuit diameter, tubing brand, and filter, in order to ensure equivalency in conditions. Results showed a maximum flow rate error of 0.5296%, highlighting the integration of control and characterization methods that enhance system precision, dependability, and reproducibility—critical factors for ensuring the safety and effectiveness of hemodialysis treatments.

This study presents the design and evaluation of AquaSignalNet, a deep learning-based system for recognizing underwater visual commands to enable the autonomous navigation of a Remotely Operated Vehicle (ROV). The system is built on a ResNet50 V2 architecture and trained with a custom dataset, UVSRD, comprising 33,800 labeled images across 12 gesture classes, including directional commands, speed values, and vertical motion instructions. The model was deployed on a Raspberry Pi 4 integrated with a TIVA C microcontroller for real-time motor control, a PID-based depth control loop, and an MPU9250 sensor for orientation tracking. Experiments were conducted in a controlled pool environment using printed signal cards to define two autonomous trajectories. In the first trajectory, the system achieved 90% success, correctly interpreting a mixed sequence of turns, ascents, and speed changes. In the second, more complex trajectory, involving a rectangular inspection loop and multi-layer navigation, the system achieved 85% success, with failures mainly due to misclassification resulting from lighting variability near the water surface. Unlike conventional approaches that rely on QR codes or artificial markers, AquaSignalNet employs markerless visual cues, offering a flexible alternative for underwater inspection, exploration, and logistical operations. The results demonstrate the system’s viability for real-time gesture-based control.

This article proposes an adaptive neural PID controller based on Lyapunov theory, called LAN-PID, for high-precision tracking of position and velocity reference trajectories in motor-gearbox systems subject to nonlinearities, friction, parametric uncertainties, and external load disturbances. A structural symmetry representation of the electromechanical system is introduced via a structural correspondence between the rotational variables of the motor shaft and those of the gearbox output shaft. This correspondence is established through a transformation determined by the gear ratio, which relates the angular position, angular velocity, and transmitted torque, providing an equivalent representation that preserves the structural relationships between the coupled rotational subsystems. In this context, structural symmetry is interpreted as a structural correspondence between the system’s dynamic variables, without assuming that the motor and gearbox have identical physical parameters or that the model is strictly invariant under a mathematical transformation. The proposed LAN-PID architecture combines adaptive PID control with a feedforward artificial neural network, which continuously adjusts the controller gains according to the system’s operating conditions. A Lyapunov-based adaptation law is derived for the neural network weights, and the resulting discrete-time Lyapunov analysis is used to evaluate the bounded behavior of the adaptive signals and the closed-loop system under the considered operating conditions. Initially, the controller is evaluated in MATLAB/Simulink using a nonlinear model of the motor-gearbox system that incorporates realistic parameters, friction effects, and load disturbances. Subsequently, the proposed strategy is implemented experimentally on a physical motor-gearbox platform, where signal acquisition and control are performed in real time using LabVIEW and a Raspberry Pi Pico W. The experimental evaluation considers different operating conditions and external loads corresponding to masses of up to $9\mathrm{kg}$ ($19.84\mathrm{lb}$). Compared to the conventional fixed-gain PID controller, the proposed LAN-PID reduced the experimental tracking RMSE by $66.7\%$ under no-load conditions and by $81.1\%$ under the evaluated load condition of $9\mathrm{kg}$, in addition to improving transient response and adaptation to changes in operating conditions. These results indicate that integrating a structural symmetry representation of the electromechanical system with a neural adaptation mechanism based on Lyapunov theory provides an effective framework for high-precision real-time control and trajectory tracking in motor-gearbox systems subject to nonlinearities, uncertainties, and external disturbances.

This paper presents the development of the digital twin of an electro-pneumatic station composed with 3 pistons, to implement this station in virtual laboratories. The objective of this station is to have an economical alternative for the accomplishment of practices of automation that counts on the capacity to connect through Ethernet with PLCs of the mark of Allen Bradley and with all the PLCs that communicate under the protocol Modbus TCP. Later, in order to develop the digital twin presented in this paper, an electro-pneumatic physical station was taken from the Mechatronics laboratory at the Universidad Tecnológica de Queretaro, the development of this digital twin was done through the free software Blender, complementing it with programming in Python for the Ethernet TCP communication with the different programmable logic controllers. Finally, we carried out the validation of this project through the connection of both the digital twin and the physical station taken as a model, with a ControlLogix PLC. This work will allow the future to implement low-cost virtual laboratories in universities, training centers and interested institutions. The laboratories will have multiple practice scenarios that result in students better prepared in the programming of automated systems in the industry.