In this article proposes methods for synthesizing control algorithms to solve the problem of tracking the course of a surface vessel. The problem of maintaining the vessel's course was solved using various algorithms. The proposed algorithms can be used in the future to solve dynamic positioning problems and route control problem.

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.

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.

Object detection is a technology capable of finding instances of objects in images. Among the main applications in many different areas the following can be highlighted: traffic monitoring, robots and security. A typical object detection algorithm takes an image as an input, finds the object of interest in it, and provides a bounding box. The output bounding box is usually axis-aligned, and it is enough for many applications. However, there are situations when the angle of rotation of the bounding box is essential and should be calculated. In this paper, different approaches for local positioning of a single object with obtainment of center coordinates and direction angle are studied and tested. Firstly, a basic image processing approach that uses binarization by the color range of the object of interest is considered. Furthermore, this simple algorithm finds the final bounding box with an angle of rotation using geometrical properties of the surface vessel. This approach obtains angle by getting the minimum bounding box using the contour of the surface vessel and principal component analy-sis. In addition, deep learning segmentation approaches were considered. Considering all algorithms, the image processing approach based on threshold binarization performs faster than deep learning approaches, suiting into the problem of local positioning of a surface vessel in a suitable way. Moreover, it provides comparable accuracy and execution time with the deep learning segmentation approaches. The final algorithm obtained can detect the surface vessel and obtain its linear (x, y) and angular (a) coordinates. It is robust to the appearance of other objects with the same colors as the object of interest and of smaller size. It is also robust to the appearance of objects of any size not in the color range of the object of interest.

The problem addressed in the paper is twofold. First, the estimation of unknown robot parameters is carried out using three different approaches, namely the gradient descent method, extended Kalman filter, and dynamic regressor extension and mixing, to evaluate their performance as applied to the two-link planar elbow robot arm. Second, an indirect adaptive inverse dynamics controller based on the obtained estimates is designed to study performance achieved by the estimation methods in the control problem. The obtained results show advantages of the dynamic regressor extension and mixing in the both addressed problems.