This study delves into the intricate realm of adaptive control systems, highlighting their indispensable role across many industries, including robotics, automation, and manufacturing. Amidst industrial environments' dynamic and often unpredictable nature, the seamless fusion of fuzzy logic principles with cutting-edge computational methodologies, such as artificial intelligence and machine learning, emerges as a beacon of efficiency and reliability. Within this context, the research unveils the profound impact of fuzzy logic algorithms in steering Cartesian robots through mechanically challenging scenarios, where transmission impairments pose significant hurdles. By orchestrating meticulous parameter adjustments, these algorithms exhibit a remarkable ability to not only mitigate structural vibrations but also to optimize operational performance in real time. Moreover, the study underscores the inherent adaptability and versatility of fuzzy logic-based control systems, empowering industrial ecosystems to navigate the complexities of ever-evolving operational landscapes with unparalleled resilience. As such, the findings shed light on the transformative potential of adaptive control systems, offering a glimpse into a future where intelligent algorithms seamlessly harmonize with mechanical systems to drive innovation and efficiency in industrial settings.

Electrical machines play an important role in modern industry. It has brought significant technological advancements, particularly in integrating information technology with physical devices. This fusion has led to the emergence of smart devices and the Internet of Things, transforming industrial operations. However, despite the popularity of predictive maintenance, there remains a notable gap in fault prediction algorithms for electrical machines. This paper proposes a signal spectrum-based machine learning approach for fault prediction, specifically focusing on mechanical bearing faults. Comparing traditional neural network algorithms with a novel approach integrating fuzzy logic, the study demonstrates that the fuzzy-neuro network model outperforms traditional neural networks, achieving a validation accuracy of 99.98% compared to 89.91%. Incorporating fuzzy logic within the neural network framework offers advantages in handling complex fault combinations, showing promise for applications requiring higher accuracy in fault detection.

This paper presents a comprehensive analysis of bearing fault impact on the vibration of brushless DC motors. This type motors are gaining heightened popularity because of their compact size, low cost, high torque, better control, and easy maintenance. They are becoming increasingly popular in low power applications such as electric scooters and robots. Among others, the bearing related faults are very common in these machines. This is because of the nature of their applications and working environment. In this paper, the effect of different bearing faults on the vibration spectrum is investigated, which can be used as a potential fault indicator. The vibration signals with different faulty bearings are measured in a laboratory setup and the spectrum analysis is done using Fourier analysis.

The paper describes the interaction between Tallinn University of Technology (TalTech, Tallinn, Estonia) and ITMO University (St. Petersburg, Russia) based on recently signed agreement. The purpose of the agreement is to record the intention of TalTech and ITMO University to cooperate on implementing a double master’s degree program, which involves confirmation of the master’s educational level absorption and the award of master’s degree and obtention of two diplomas, and to outline the organizational form, principles and terms of the implementation. The double degree Program is a two-year Master’s program of 120 ECTS credit points. Its structure and content meets the requirements that both TalTech and ITMO University have established for their master’s programs, in accordance with the government standards. Development and approval of the double degree Program is held by the Parties in accordance with the procedures established by them. This paper describes the procedure of implementation of a double-degree master program in mechatronics engineering.