Servomotors have found widespread application in many areas, such as manufacturing, robotics, automation, and others. Thus, the control of servomotors is divided into various principles and methods, leading to a high diversity of control systems. This article provides an overview of types of servomotors and their basic principles and control methods. Principles such as digital signal processing, feedback control principle, field-oriented control, and integration with Industry 4.0 are discussed. Based on these control principles, the article presents popular control methods: PWM control, current control, two-loop control, fuzzy-logic control, and programmable control. The article concludes with a comparison of the presented methods on several criteria, and as an example, it includes the results of modeling a servomotor using the fuzzy-logic control method.

Collaborative robots (cobots) play a crucial role in modern industry by ensuring safe and efficient human–robot interaction. However, traditional control methods struggle with uncertainty handling and adaptability to dynamic environments. This review explores the application of fuzzy logic as a promising approach for cobot control. The article discusses the fundamental principles of fuzzy logic, its advantages over classical methods, and successful case studies. It analyzes current research, including hybrid methods combining fuzzy logic with machine learning and evolutionary algorithms. The paper also highlights existing challenges and potential future research directions. The conclusions emphasize the potential of fuzzy logic to enhance cobot adaptability and reliability in real-world conditions.

Faults, which occur during the work of industrial robots, have different natures and characteristics. They emerge due to the continued hard work of the control system and mechanical parts. Diagnosis of each fault type is an important task because preliminary faults detection precludes expensive equipment replacement. In this paper, the significance of timely break detection is highlighted. Also, the possibility of fault elimination is displayed. Transmission failures and diagnosis methods, which can be used for fault elimination, are presented. In this report, a description of the Hirata cartesian robot, its diagnosis system, and the parameters of the robot are declared. This article presents experiments with belt tension of tooth belt transmission installed in the Hirata cartesian robot.

The article considers a method for increasing the productivity of a robotic manipulator based on eliminating emerging, undesirable disturbances. It is talk about the non-standard method of reducing the travel time using optimal regulation and the synthesis of the control system based on the results obtained.

The article considers the possibility of using vector control (FOC) with optimal regulator to raise/lower parts by an auto operator in a galvanic line and also for braking process without deviation hanger with details. We consider the rigid regulation of speed, as well as the ability to quickly and smoothly switch the speed from one mode to another without sudden jumps. The article compares the use of vector control with the optimal regulator and without it. Also shows the calculation and synthesis of the control system of the auto operator using the package Matlab.

The detection of faults during an operational process constitutes a crucial objective within the framework of developing a control system to monitor the structure of industrial mechanisms. Even minor faults can give rise to significant consequences that require swift resolution. This research investigates the impact of overtension in the tooth belt transmission and heating of the screw transmission worm on the vibration signals in a robotic system. Utilizing FFT techniques, distinct frequency characteristics associated with different faults were identified. Overtension in the tooth belt transmission caused localized oscillations, addressed by adjusting the acceleration and deceleration speeds. Heating of the screw transmission worm led to widespread disturbances affecting servo stress and positioning accuracy. A fuzzy logic algorithm based on spectral analysis was proposed for adaptive control, considering the vibration’s frequency and amplitude. The simulation results demonstrated effective damage mitigation, reducing wear on the mechanical parts. The diagnostic approach, relying on limited data, emphasized the feasibility of identifying transmission damage, thereby minimizing maintenance costs. This research contributes a comprehensive and adaptive solution for robotic system diagnostics and control, with the proposed fuzzy logic algorithm showing promise for efficient signal processing and machine learning applications.