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Fast and accurate detection of emerging faults in synchronous generators, which have found wide application in power and transport systems, contributes to ensuring reliable operation of the entire system. This article presents a new approach to making accurate decisions on the continuation of the operation of damaged generators in accordance with the requirements of IEEE standards. The necessity of limiting the duration of operation of the generator in conditions of asymmetric short circuits in the stator windings is substantiated. The authors of the article, based on an artificial neural network in the Matlab software environment, have developed a model for detecting, classifying, and making quick and accurate decisions about the operation of the generator in the event of asymmetric short circuits in the stator windings of the generator. This makes it possible to simulate the operation of the generator at various parameters. Prior to training the neural network, the database formed by phase current and voltage signals was analyzed by various features. The neural network was trained using the back-error-propagation algorithm. The output 10 neurons of the network showed the state of the phase windings of the stator. The recorded information of the output neurons was evaluated, in terms of meeting the requirements of the IEEE standard, and decisions were made about continuing or interrupting the generator operation. Tests of the effectiveness of the model showed that it could achieve the desired result at step 49, and the calculated accuracy was 99.5833%. The results obtained can be successfully used in the development of high-speed and highly reliable diagnostic systems and control and decision-making systems for generators for various purposes.

The application areas of robot manipulators are increasingly growing year by year. Rehabilitation exoskeletons and upper limb prostheses are special cases of robot manipulators. Such systems, being nonlinear, multidimensional, and dynamic systems, have parametric uncertainties. All this becomes a challenge in the design process of the control system. One of the advanced technologies in this field is adaptive control, which is an important and interesting area of scientific and industrial research. The nonlinear control system of the two-linked robot manipulator was considered in the work. In this case, classical control methods become inapplicable and there is a need to develop and apply other control methods. Using the gain scheduling method, a group of PID controllers are designed to ensure the stability of the system at various operating points. The non-linearity of the system is compensated by the gain scheduling method and the only problem that remained is the uncertainties of the system depending on the weight of the operator. A model reference adaptive controller (MRAC) was designed and augmented with the gain scheduling method for eliminating uncertainties. Simulation is done in the MATLAB Simulink environment. A comparative performance has been quantified with different adaptation gains for the upper limb prosthesis system. The obtained results show the viability of the proposed control method.

The use of the bionic hand requires an extensive training procedure which is a major challenge for patients. The patients need to learn to control the bionic hand before starting using it, therefore, training should be done efficiently. One of the proposed methods is controlling the virtual bionic hand via physical EMG (electromyography) sensors. In general, one of the main problems of any prosthesis is the classification of the patient's finger movements. For this reason, some well-known machine learning algorithms are discussed. Comparative analysis of machine learning algorithms is performed, the best-selected algorithm is used for the system later. The classifier for finger movement classification is trained and tested. The virtual model of the bionic hand has been developed. The kinematics of bionic fingers is analyzed. The bionic finger performs tasks in the Cartesian space, whereas actuators work in the Quaternion (joint) space. It is necessary to transform the coordinate system from Cartesian to joint space and vice versa․ The inverse and forward kinematics is obtained by using the geometry approach and the Denavit - Hartenberg (DH) methods accordingly. The control system is designed for the virtual bionic hand model. The developed method gives an opportunity to classify all the movements of fingers via two surface EMG electrodes with an ML (Machine learning) based or the NN (Neural Network) classifier, and to control the designed bionic hand model in the MATLAB / Simulink environment.