The paper considers the problem of cooperative control synthesis for a complex of $N$ underwater vehicle–manipulator systems (UVMS) to perform the work of moving a cargo along a given trajectory. Here, we used the approach based on the representation of nonlinear dynamics models in the form of state space with state-dependent coefficients (SDC-form). That allowed us to apply methods of suboptimal control with feedback based on the state-dependent differential Riccati equation (SDDRE) solution at a finite time interval, providing the change in control intensity with the transient effect of the system matrices in SDC form. The paper reveals two approaches to system implementation: a general controller for the whole system and a set of $N$ independent subcontrollers for UVMSs. The results of both approaches are similar; however, for the systems with a small number of manipulators, the common structure is recommended, and for the systems with a large number of manipulators, the approach with independent subcontrollers may be more acceptable. The proposed method of cooperative control was tested on the task of cooperative control for two UVMSs with six-link manipulators Orion 7R. The simulation results are presented in the article and show the effectiveness of the proposed method.

This paper proposes a maximum-ratio combining sensor fusion scheme for using an extended Kalman filter in the underwater vehicle positioning task by means of communication devices (buoys) providing location information using a slant-range mechanism, inertial sensors, a Doppler velocity log, and a pressure sensor in the absence of bearing angle data. The parameter estimation methods for all navigation system components are described. The results of simulation modeling with corresponding quality metrics are presented. The outcomes were supported by conducted field experiments. The results obtained allowed us to obtain a position determination model for the underwater vehicle, which is still a relevant and complex task for seabed explorers.

The paper focuses on the optimal state-estimation algorithm for discrete-continuous systems. The research aim is to create an effective strategy for combining data from continuous and discrete information sources to improve the state estimation accuracy and reliability of complex dynamic systems. The paper discusses, in detail, the theoretical foundations of the proposed method, including the mathematical description of continuous and discrete models, and its optimality criterion formulation. State-vector augmentation is proposed to improve the estimation convergence. The authors present numerical modeling results demonstrating the algorithm’s efficiency on the example of motion parameter estimation for the autonomous underwater vehicle. The conclusions are drawn about the promising application for the developed algorithm in various fields related to information processing in complex technical systems, such as navigation, motion control, and state and processes monitoring. It is noted that the proposed approach can be generalized to the case of more sources’ fusion. The paper is considered to be valuable for specialists in control theory and signal and information processing, as well as for navigation and motion-control system designers. The results obtained may find practical application in the development of high-precision state-estimation systems in various technical applications.

The development of an underwater vehicle model is an important preparation step for potentially marine exploration. There are several complex factors that hinder the performance of this task, e.g., fluid and rigid-body physics. Moreover, real vehicles have different designs and dimensions. That fact complicates the calculations of dynamic parameters and requires the use of known approximation methods. This article discusses the research of uncertainties in the parameters of two different forms of underwater vehicle model approximations: cylindrical and rectangular prism. We simulate an underwater vehicle with several parameters, changed in the grid, and then compare the resulting paths. The mathematical models and simulation results are shown.

This paper applies the MIMO NARX neural model to identify AUV with six degrees of freedom. Strongly nonlinear dependencies are modeled using a MIMO NARX model trained using «input-output» experimental data. This results in a grey box model with a partially known internal structure that can be supervised. This is achieved by specifying a certain regressors type, which is feedback to the model input. Obtained results show that the neural model trained by the Levenberg–Marquardt numerical algorithm provides acceptable performance and accuracy. Validation of the model was performed using data from full-scale experiments, which allowed to identify the weaknesses and advantages of the resulting system more accurately.

To create a platform for the Marine IoT for ensuring the interaction and digital navigation of heterogeneous marine robotic agents, it is necessary to combine into one network heterogeneous robotic agents, active and passive means of communication and illumination of the underwater and surface environment (marine communication buoys, underwater sonar stations, coastal connection posts). For their joint work, it is necessary to create a communication system unifying all elements of the platform, an integrated navigation and information management system for solving complex problems of navigation and motion control of robotic agents included in the platform to implement a joint mission. To do this, it is necessary to define the architecture of the platform to use a unified approach to data exchange.