This paper presents a nonsingular terminal sliding mode control (NTSMC) strategy for robust three-dimensional (3D) trajectory tracking of a quadrotor unmanned aerial vehicle (UAV) operating in the presence of external disturbances and modeling uncertainties. The proposed controller is designed using the full nonlinear dynamics of the quadrotor and ensures finite-time convergence of the sliding variables and tracking errors without introducing singularities in the control law. A Lyapunov-based stability analysis is provided to demonstrate finite-time stability of the closed-loop system. The effectiveness of the proposed approach is evaluated through comparative simulations against representative state-of-the-art sliding mode control strategies. Numerical simulations are conducted under multiple operating scenarios, including large initial deviations and persistent external perturbations. The results demonstrate that the proposed NTSMC scheme achieves improved tracking accuracy, enhanced robustness, and a more favorable transient response compared to the considered benchmark controllers.

This paper presents a concise, application-focused description of using EEZ Studio (Envox Experimental Zone) as a practical environment for system identification, data acquisition, and implementation of controllers on the programmable power supply EEZ BB3. The work demonstrates how EEZ Studio integrates SCPI control, MicroPython/JS scripting, and direct interaction with instruments (oscilloscopes and BB3) to perform identification and closed-loop control. The experimental implementation (water-level and DC-motor speed control tasks) used the EEZ BB3 as the actuator with an STM32 Nucleo micro-controller for signal acquisition and UART communication. The results show reliable acquisition, effective controller deployment in the BB3 and stable closed-loop behavior, indicating that EEZ Studio is a feasible tool for rapid prototyping and educational control experiments.

This paper presents a finite-time super-twisting sliding mode control (STWSMC) framework for robust three-dimensional (3D) trajectory tracking of a quadrotor unmanned aerial vehicle (UAV) operating under exogenous disturbances. The proposed approach ensures continuous control action while preserving finite-time convergence properties. A complete non-linear dynamic model of the quadrotor is considered, including translational-rotational couplings and gravitational effects. Separate STWSMC structures are developed for the altitude and attitude subsystems, guaranteeing robustness against bounded disturbances and model uncertainties without requiring explicit disturbance estimation. A Lyapunov-based stability analysis is carried out, proving finite-time convergence of the sliding variables and finite-time stability of the closed-loop tracking errors. Simulations demonstrate improved transient performance, reduced chattering amplitude, and enhanced robustness—particularly in yaw dynamics—when compared to a conventional second-order sliding mode control (SOSMC) scheme. The obtained results indicate that the proposed STWSMC strategy provides a theoretically sound and practically viable solution for high-performance quadrotor control.

This article explores the robustness and explainability of a convolutional neural network-based fault detection method for medium-voltage circuit breakers. The robustness is analysed by evaluating the method's performance under the presence of stationary and non-stationary disturbances in the vibration signature. Additionally, the impact of sensor ageing on performance indices is investigated to assess long-term reliability. Since the condition assessment method is focused on binary classification, the detection outcome interpretation aspect is addressed by providing recommendations for operator or autonomous system actions. Both aspects are demonstrated using datasets collected from real-world medium-voltage circuit breakers.

Motion tracking achieved via conventional video processing and machine vision algorithms is often hindered by challenges such as motion blur and the lack of distinctive visual features, particularly when tracking fast-moving objects. To address these limitations, active visual markers are often used. In this paper, we present the design and prototype implementation of an active marker that is compact, detachable, and self-powered, making it well-suited for real-world tracking applications. Furthermore, the marker is fully configurable through an accompanying software solution and an additional wireless communication controller via an infrared protocol. The applicability of the developed markers is demonstrated using both conventional RGB and event-based cameras, highlighting their versatility and robustness across diverse sensing modalities. Their tracking capabilities are validated in both single- and multi-object scenarios. Overall, the developed multi-functional markers provide a flexible and practical foundation for high-speed motion tracking under challenging visual conditions, paving the way for further research and advanced applications in related fields.

Timely and accurate defect detection is essential in the leather industry, as the quality of raw leather directly impacts both the usability and value of finished products. This paper provides a systematic overview of state-of-the-art solutions and proposes a novel approach for automated detection of leather surface defects using deep neural networks based on the Inception-V3 architecture. Five defect categories are introduced, focusing on their impact on leather quality. In addition, two deep neural network architectures were analyzed and implemented for defect detection and classification: a single-channel model and a multi-channel model with arbitration. The evaluation was carried out using a combination of a custom-developed dataset and publicly available datasets, assessed with standard performance metrics. Moreover, an image annotation tool was developed to facilitate precise defect labeling and the creation of variable-size datasets. Both models demonstrated promising results on the custom dataset, achieving accuracy rates exceeding 93%. The suggested methodology enhances the research domain of leather inspection automation by creating an openly accessible image dataset, performing a comparative analysis of detection models and creating software tools for data preparation. These contributions lay the foundation for further research in leather defect detection and potential industrial implementation.

This paper focuses on the design and implementation of a discrete digital PID (Proportional - Integral - Derivative) controller utilizing an FPGA (Field Programmable Gate Arrays) platform, which inherently supports parallel implementation of algorithms. Typically, cost-effective FPGA boards lacks peripherals, such as analog inputs and outputs, so they need to be added externally. The main hypothesis is that a DC motor system can be controlled with a low-cost variant of FPGA-based PID controllers. Therefore, an I2C (Inter-Integrated Circuit) based AD (Analog-to-digital) converter is added as input, while PWM (Pulse width modulation) based output signal is used as an output. The effectiveness of the designed regulator is demonstrated on an example of a DC (direct current) motor control. Additionally, for control and monitoring purposes, the FPGA is connected to the PC using the UART (Universal Asynchronous Receiver Transmitter) protocol. Experimental results indicate that the FPGA-based PID implementation offers solid performance.

This paper presents the development and implementation of a flexible industrial machine model for automated visual inspection, called ETFCam, designed to improve the learning outcomes of electrical engineering students in the field of machine vision and robotics. Unlike prefabricated didactic models, which are typically “closed” systems with a predefined set of experiments, custom didactic systems for teaching and training built from scratch tend to be more flexible and provide a deeper insight in engineering, machine design and planning, while being more cost-effective. The proposed system is based on a 3DOF stepper motor-based manipulator, a DC motor driven conveyor, a pneumatic actuated gripper and a machine vision system. The paper discusses several applications of such a system in an educational environment, with a special focus on machine vision applications. Due to the fact that the system is versatile, open, modular, and easy to upgrade, it has unlimited potential and possibilities for further development. In addition, it provides students with a perfect testbed for learning new engineering skills in many areas such as schematic drawing and understanding, PLC based control, sensing, and machine vision.