This study focuses on detecting and isolation of faults in the inertial navigation system (INS) of an unmanned surface vessel (USV) which parameters are unknown. The INS comprises units for measuring angular and linear velocity. The proposed method employs full-order Luenberger observers to construct directional generators of residual signals. To determine the unknown parameters of the USV, Kreisselmeier’s dynamic extension of the regressor is applied that assists in maintaining the excitation level of the original regressor. The latter is supplied with an ad-hoc modification resulting in a fixed time convergence of estimation errors to zero. Through simulations, the study demonstrates the effectiveness and reliability of the algorithm in accurately identifying faults in each sensor and the improvement of transients of parameter identification compared to DREM-based estimator.

The article aims to research the application of neural networks in signal adaptive filtering. The problem of filtering signals from noise and distortion is relevant in control systems. In this paper, white noise filtering using adaptive filters and neural networks is reviewed. Neural network algorithms were chosen to solve the problem of signal filtering. Neural networks and classical adaptive filtering algorithms, such as the least mean squares and the recursive least squares, were compared considering their efficiency for additive white noise filtering tasks. A multi harmonic signal was filtered from the additive white Gaussian noise using these approaches. As a result, classical adaptive filtering algorithms demonstrated better performance in signal filtering tasks.

The control design problem for finite-time stabilization of linear descriptor systems is considered. The scheme of control parameters selection is presented by a linear matrix inequalities. Obtained stabilizability conditions are more reliable (linear matrix inequalities are not fragile) than the existing results. The settling time estimate is obtained. The theoretical results are supported by numerical simulations.

The paper considers the problem of sensor faults detection and isolation under external disturbances for a linear multi-output system. A special disturbance observer is used for its value online estimation on the base of input signal and measured output. Adaptive modification provides disturbance generator identification. Disturbance estimates are used for synthesis of plant full state observer. Fault detection is based on the residual bank that consists of differences between plant state estimates and measured outputs. Fault isolation is based on directional residual set. Robustness with respect to the noises, non-regular terms of disturbance and parametric uncertainties is provided by thresholds and directional relationships technique. The simulation results confirm the performance of the proposed approach for the third order plant.

The paper describes an algorithm for localization and classification of quadcopter sensor (accelerometer and gyroscope) failures. Based on the obtained attributes; the conclusion is made about the serviceability of a quadcopter-type unmanned aerial vehicle (UAV). Two machine learning methods are used: logistic regression and random forest method. Their performance evaluated and compared using the data obtained by simulating the physical model of a quadcopter. A computer simulation of position sensor failure detection is presented. The proposed approach has shown its effectiveness and, therefore, it can be used to build a fault-tolerant adaptive motion control subsystem for UAVs.

In this paper, the authors examine the importance of a transdisciplinary approach to cyber-physical systems training. The article concludes that it is necessary to introduce new educational models that will contribute to the formation of innovative thinking of master students. The use of a multidisciplinary approach in the training of master students is substantiated, and a combined scheme of interdisciplinary and multidisciplinary methods is proposed on the example of the disciplines Cyber-physical systems and technologies. Specialization lies in the fact that not only the available baggage knowledge, but also ways to find their knowledge in a new application, including in non-standard conditions, readiness for self-development and improvement information. The ability of the current educational model to meet the requirements has been established.

The paper is aimed to the robust control of parametric uncertain linear plants in conditions of signal quantization with time delay and bounded external perturbations. State vector of control plant is unmeasured. Output of control plant is measured only via quantizer with transport delay. Control algorithm is based on consecutive compensator method. Proposed algorithm provides convergence of tracking error to the reference signal with prespecified accuracy for bounded time delay in quantizer. Limitations on value of delay providing stability of closed-loop system are obtained.

This paper addresses to the problem of a non-linear MIMO systems control. A class of non-linear parameter uncertain systems operating under unknown bounded disturbances is considered. It is assumed, that mathematical model of such system can be decomposed on linear and non-linear dynamics. Proposed control algorithm is based on the method of consecutive compensator. The only required parameter to be known for the controller synthesis is a relative degree of linear part of plant. The effectiveness of the control method is demonstrated experimentally using the laboratory platform named «Twin Rotor MIMO System». The proposed method is compared with standard PID controller. Experimental results show that the transient behaviour of the developed control algorithm provides higher accuracy and performance, especially for the case of model parameters deviation from their nominal values.

The article describes an active learning method in studying the course "Integrated Systems Design and Control" for the masters program "System Analysis and Control". The method is based on involving of students to acquisition of a new knowledge in learning process, finding optimal solutions supported by practical activities. Strengthening of theoretical knowledge is supposed to be performed through workshops and discussions of the learnt materials. Practical learning is performed within the laboratory practice on a specially designed bench that described in this article.