In this paper, an experimental study of robust trajectory control algorithms under incomplete information about the parameters and state variables of a nonlinear motion model of a mobile robot is carried out. In this study, we have synthesized and tested on a real holonomic mobile robot a set trajectory control algorithm with measured adjustable variables, set trajectory control algorithm in the absence of measurements of adjustable variables and measured robot coordinates. In addition, an algorithm for controlling the motion of a mobile robot along a given trajectory in the absence of measurements of adjustable variables and robot coordinates was tested.

The problem in this paper is addressed to output regulation of saturated plant. Anti-windup technique used to prevent overshooting and oscillatory processes in system with physical signal limitations. The robust output control framework was modified in order to take into account physical properties of the plant. Simulations show effectiveness of the proposed method in case of motor regulation in the presence of disturbances and non-static gain coefficient.

The paper focuses on using Deep Learning (DL) and Computer Vision (CV) techniques to detect surface defects in rolled metal products. By utilizing a Convolutional Neural Network (CNN), various surface defects can be detected and recognized, ultimately improving production standards and certification of the metal products. Two types of lighting, diffuse and side, are used to improve defect detection, and image preprocessing methods are employed to enhance the quality of the input data. The purpose of this work is to develop a method for recognizing and classifying defects in metal surfaces from their images in real time.

In this paper experimental research on the output control problem of a surface vessel with parametric uncertainties and external disturbances is shown. We propose application of the robust control framework based on the extended observer design with subsequent augmenting with the internal model to reject external disturbances affected the robotic boat in laboratory basin. Dynamic Positioning (DP) mode includes task of a station-keeping which can can be effectively solved with proposed algorithm.

In this article dynamically positioned surface vessel model is considered. To achieve precise performance of the vessel with unknown parameters two control problem was solved. First one is online parametric identification. The estimation of unknown parameters of the surface vessel model is obtained by methods, which are gradient descent method, extended Kalman filter and dynamic regressor extension and mixing (DREM). Second problem is the design of the adaptive control based on the collected results from estimators. For this purposes inverse dynamics control is considered. The convergence of the system is shown in the modeling section. DREM estimation method showed best results.

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.

The problem of estimating the parameters of deterministic signals with constant parameters is considered. The main idea is to parameterize the signal using delays and obtain a linear or nonlinear regression model. At the first stage, the signal is presented as the output signal of a linear generator of finite dimension. At the second stage, the Jordan form of the matrix and the delay operator for parameterization is applied. The performance of the algorithms considered in this article is illustrated by computer modeling.

An adaptive algorithm, compensating for unknown multiharmonic disturbance acting on linear objects under conditions of the unavailable state vector with a defined delay in the control channel is proposed. A new approach is proposed for estimating the frequencies of a multiharmonic signal. It is assumed that all parameters of the multiharmonic disturbance are unknown. The task is completed in several steps. First, an observer is constructed based on a frequency estimation scheme. Secondly, stabilization of the state of the object to zero is carried out using feedback based on the predictor. Examples are given to demonstrate the efficiency of the proposed algorithm. Our main contribution is to propose a new scheme for compensating external disturbances for a linear plant and a new approach for estimating the frequencies of a multisinusoidal signal.

Identification of gait parameters is one of the most important tasks allowing to estimate stability of walking aid devices usage case and biped robots. The estimation approach has been proposed and used to estimate model parameters using the foot trajectory. This method uses only foot coordinates of captured data and shows fast estimation. Efficiency of proposed approach is demonstrated by simulation as well as experimental results in comparison with another well-known method. An approach of the data preprocessing is applied to make motion captured data suitable for estimation. Estimated parameters of the model could be applied to estimation of walking robots stability and human walking in case of aid devices usage.

This paper focuses on the applied research on the output control problem of a surface vessel with parametric, signal, and structural uncertainties. The control framework based on the extended observer design is augmented with the internal model to reject exogenous inputs such as reference derivatives and external disturbances. Special attention in this study is given to the presence of unmodeled dynamics due to the vessel actuators.

A new path planning algorithm for industrial robots is proposed in this paper. The algorithm is based on arc approximation of the given reference path. Such solution allows to reduce amount of the reference points, decrease code size and computational costs, increase performance of operations and simplify programming complex movements. The study represents extension of the previous work to the case of planning spatial movements in three-dimensional space. Experiments using a 6-DOF (degrees of freedom) articulated robot simulating typical industrial operations (e.g. welding, engraving, polishing) were carried out and corresponding experimental results are reported in the paper.

This paper is a result of the university-industry collaboration between ITMO University and Thermex. A subject of this cooperative investigation is robotic automation of industrial operations in the sense of organizing a cyber-physical system and reducing the human factor from the production. Tasks of a technological process considered in the paper are welding, transporting and polishing. They are simulated on the basis of the laboratory of the Department of Control Systems and Informatics. Robots used in this study are Mitsubishi MELFA RV-3SDB, KUKA youBot and Kawasaki FS06N. Three control levels are highlighted in the paper. There is the strategic level at the top. Its main part is a central control software based on MATLAB used to set up a network and coordinate robots between the operations. Control of a separate robot corresponds to the tactic level. Finally, the local level is assigned to control of actuators and get data from sensors.