Control Of Autonomous Underwater Vehicle Research Articles

Distributed leaderless formation control for multiple autonomous underwater vehicles based on adaptive nonsingular terminal sliding mode

In this study, we address the formation control problem of multiple autonomous underwater vehicles (AUVs) in the presence of model uncertainties and ocean-current disturbances. Specifically, we propose a robust distributed leaderless formation control strategy by cross-coupling a synchronous control technique, a nonsingular terminal sliding-mode control approach, and an adaptive control method. We enhance the robustness against system uncertainties by redefining the cross-coupling errors and ensure that the finite-time tracking error converges to zero from any initial value via the designed adaptive nonsingular terminal sliding mode control, which also eliminates the chattering problem. A major advantage of our adaptive control law is neglecting the requirement of any prior knowledge about the upper bounds of the model uncertainties and ocean-current disturbances. Our comparative simulation results demonstrate the effectiveness of the proposed distributed leaderless formation control scheme.

The future of autonomous underwater vehicle control

The future of autonomous underwater vehicle control

Admissibility analysis for autonomous underwater vehicle based on descriptor model with time-varying delay

This article investigates an enhanced optimal robust time-delay stabilizer for an autonomous underwater vehicle in the descriptor model. Time-delay, model uncertainty, and actuator saturation constraint are some practical challenges in autonomous underwater vehicle controller design. In this regard, an appropriate autonomous underwater vehicle descriptor model is obtained, and sufficient stabilization conditions are determined in the terms of linear matrix inequality. The obtained criterion guarantees the system to be regular, impulse-free, and stable. Meanwhile, the delay-dependent and rate-dependent conditions are taken into account. Furthermore, uncertainty and time-delay are time-variant. This method includes a tuning factor for practical design aspects and tradeoff among desired requirements. Also, as an essential general requirement in non-linear systems, the maximal estimate of the attraction domain is proposed as an optimization problem. Numerical examples and simulations illustrate that the proposed methods are effective and useful in less conservative results. The technique can be generalized and applied to the most conventional autonomous underwater vehicles.

Discrete-Time Position Control for Autonomous Underwater Vehicle under Noisy Conditions

This paper deals with the discrete-time position control problem for an autonomous underwater vehicle (AUV) under noisy conditions. Due to underwater noise, the velocity measurements returned by the AUV’s on-board sensors afford low accuracy, downgrading its control quality. Additionally, most of the hydrodynamic parameters of the AUV model are uncertain, further degrading the AUV control accuracy. Based on these findings, a discrete-time control law that improves the position control for the AUV trajectory tracking is presented to reduce the impact of these two factors. The proposed control law extends the Ensemble Kalman Filter and solves the problem of the traditional Ensemble Kalman Filter that underperforms when the hydrodynamic parameters of the AUV model are uncertain. The effectiveness of the proposed discrete-time controller is tested on various simulated scenarios and the results demonstrate that the proposed controller has appealing precision for AUV position tracking under noisy conditions and hydrodynamic parameter uncertainty. The proposed controller outperforms the conventional time-delay controller in root-mean-square error by a percentage range of approximately 72.1–97.4% and requires at least 89.5% less average calculation time than the conventional model predictive control.

Neural adaptive output feedback tracking control of underactuated AUVs

This paper addresses the three-dimensional tracking and stabilization problems simultaneously for underactuated autonomous underwater vehicles (AUVs) subjected to parametric uncertainties, external disturbances, unmeasured states and input saturation. A modified error state formulation is introduced to produce auxiliary virtual controls for the underactuated kinematics. A new high-gain observer is proposed to estimate unavailable velocities which doesn't need the position and orientation of AUVs to be bounded. Based on the backstepping technique and combinations of high-gain observer and radial-basis-function neural network (RBFNN) approximations, the kinematic and dynamic controllers are designed to force underactuated AUVs to track a time-varying trajectory or/and a stationary point, and guarantees the tracking errors convergent to a bounded neighborhood of the origin. Some comparative simulations are presented to validate the effectiveness and performance of the proposed control scheme.

Event-triggered output-feedback adaptive tracking control of autonomous underwater vehicles using reinforcement learning

This paper investigates the event-triggered tracking control of fully actuated autonomous underwater vehicles (AUVs) in the vertical plane. Specifically, this paper studies the ETC in the sensor-to-controller channel, where the X-Z coordinates and the pitch angle are transmitted in an event-triggered manner. The greater communication saving is ensured. Additionally, the recently-raised problem of “jumps of virtual control laws” is solved by establishing an event-triggered adaptive neural observer. This observer can offer the benefit of state recovery, which conforms to the nautical practice. As the observer is co-located with the controller, a succinct triggering condition is devised to avoid the “Zeno” behavior and co-located with the sensors. To achieve the optimization of the long-term tracking performance, the reinforcement learning (RL) technique is performed by using the critic-actor method with the radial basis function (RBF) neural networks (NNs). The critic NN approximates the performance index and is transferred as the reinforcement signal to the actor NN, which accounts for the uncertainties. The closed-loop stability is analyzed based on the observer-based tracking errors, and all of them are proved to be semi-globally uniformly ultimately bounded (SGUUB). Finally, a numerical experiment substantiates the effectiveness of the proposed scheme.

АЛГОРИТМ МАНЕВРИРОВАНИЯ АВТОНОМНОГО НЕОБИТАЕМОГО ПОДВОДНОГО АППАРАТА ПРИ ПРОВОДКЕ СУДНА ЧЕРЕЗ ЗАМИНИРОВАННЫЙ РАЙОН

One of the tasks assigned to the autonomous underwater vehicles (AUV) is the fightagainst mine danger, which includes: 1) search for and destroy mines in the mined area;2) ensure their own safety when passing through the mined area or when working in this area;3) ensure the navigation of ships (including submarines) through the mined area. AUV can beconsidered as a further development of mine-fighting equipment, since they have a number ofadvantages over mine-fighting ships: 1) exclude the loss of life in the event of a mine explosion;2) have a lower level of physical fields to which the fuses of sea mines react; 3) are able to maneuverat the optimal depth for searching and classifying mines. In view of this, the creation ofspecialized AUV to combat the mine danger is very important. The search for mines with thehelp of AUV can be carried out either in the interests of their destruction (clearing the area) orfor the purpose of guiding ships through the mined area. This article deals with the secondproblem. The description of the maneuvering algorithm of the AUV when ensuring the passageof a vessel through a mined area is given. The task is solved by detecting different types ofmines using hydroacoustic and magnetometric means of searching and classifying underwaterobjects and bypassing of the mines at a safe distance. To reduce the time required to solve thetask, the detected underwater objects are classified into the "mine-like object" and "other objects"classes. The use of the "mine-like object" class, which includes both the actual mines andobjects that are indistinguishable from mines at the distances of their detection by means ofmine search, allows you to avoid approaching detected underwater objects at a distance of theirconfident classification using high-frequency sonar and television equipment, and thereby increasethe safety of the AUV and reduce the time of passage through the mined area. The algorithmtakes into account the fact that the minimum safe distance between vessel and mine greatlyexceeds the range of detection of mines of different types that does not allow AUV to find asafe vessel passage through the mined area by a single crossing and requires complex maneuveringof the AUV. Algorithm is intended for implementation in the AUV control system.

Composite learning tracking control for underactuated autonomous underwater vehicle with unknown dynamics and disturbances in three-dimension space

In this paper, a composite learning tracking control scheme is developed for underactuated autonomous underwater vehicles (AUVs) in the presence of unknown dynamics and time-varying disturbances. Line-of-sight (LOS) tracking control is employed to handle the underactuation of AUVs. The unknown dynamics of the AUVs are approximated by adaptive neural networks (NNs). The serial-parallel estimation models are built to obtain the prediction errors. Both the prediction errors and the tracking errors are employed to design the composite weights updating law. Nonlinear disturbance observers based on composite learning control are constructed to estimate time-varying disturbances. The stability analysis via the Lyapunov method indicates that the uniformly ultimate boundedness of all signals of the AUV trajectory tracking close-loop control system. The simulation results on an AUV verify the effectiveness and the superiority of the proposed composite learning tracking control scheme.

Design of super twisting disturbance observer based control for autonomous underwater vehicle

Autonomous underwater vehicles (AUVs) operates in uncertain oceanic environment with unknown external non-vanishing disturbances such as ocean currents. To handle such uncertainties, a super twisting algorithm as a disturbance observer based sliding mode controller (STA-SMC) is designed for trajectory tracking of a linearised steering and diving plane models of highly non-linear model of AUV. The efficacy of designed control scheme has been verified by comparing it with uncertainty and disturbance estimator (UDE) and disturbance observer (DO) based sliding mode control strategies. The extensive numerical simulations have been performed to demonstrate the robustness of the proposed scheme. It is found that the proposed STA-SMC scheme is not only effective in compensating the uncertainties in hydrodynamic parameters of the vehicle but also it rejects unpredictable disturbances due to fast and high magnitude underwater ocean currents. The stability of the designed observer based control scheme is provided by Lyapunov theory.

Control of Multiple AUVs by ASV

A major advantage of Autonomous Underwater Vehicles (AUVs) is that it is possible to operate a greater number of them simultaneously thanks to their autonomous and cable-less characteristics. However, the more AUVs are operating at the same time, the more difficult it becomes to control them. One of the solutions to this problem is to use an Autonomous Surface Vehicle (ASV) and this allows the mother ship to focus on ASV operations and emergency response.

Deep Reinforcement Learning for Vectored Thruster Autonomous Underwater Vehicle Control

Autonomous underwater vehicles (AUVs) are widely used to accomplish various missions in the complex marine environment; the design of a control system for AUVs is particularly difficult due to the high nonlinearity, variations in hydrodynamic coefficients, and external force from ocean currents. In this paper, we propose a controller based on deep reinforcement learning (DRL) in a simulation environment for studying the control performance of the vectored thruster AUV. RL is an important method of artificial intelligence that can learn behavior through trial-and-error interactions with the environment, so it does not need to provide an accurate AUV control model that is very hard to establish. The proposed RL algorithm only uses the information that can be measured by sensors inside the AUVs as the input parameters, and the outputs of the designed controller are the continuous control actions, which are the commands that are set to the vectored thruster. Moreover, a reward function is developed for deep RL controller considering different factors which actually affect the control accuracy of AUV navigation control. To confirm the algorithm’s effectiveness, a series of simulations are carried out in the designed simulation environment, which is a method to save time and improve efficiency. Simulation results prove the feasibility of the deep RL algorithm applied to the control system for AUV. Furthermore, our work also provides an optional method for robot control problems to deal with improving technology requirements and complicated application environments.

Optimum frequency selection for localization of underwater AUV using dynamic positioning parameters

Underwater connectivity has been a leading field of study in undersea navigation, deep-sea investigation and autonomous underwater vehicle control (AUVs). Because of the low attenuation (signal reduction) of sound in water, acoustic communication is the most versatile and broadly used tool in underwater network. Factors such as long propagation delay, restricted usable bandwidth, large Doppler range, time-varying channel conditions, pressure and various salinity conditions make the application of the underwater acoustic communication (UWAC) system difficult. Underwater naval monitoring and underwater exploration are the basic uses of Underwater Wireless Sensor Networks (UWSN). The outline of sensor networks has been resurrected into a new age of global physical object tracking with the latest advancement of innovation. This advances in design paved the way for new unopened insider information to be revealed in the area of underwater ecosystems, deep water conditions and discovery of ice sheets. This work provides a proficient packet transmitting technique in a selective frequency to increase the coverage, synchronisation and connectivity between sensor AUVs that are under secluded ocean contour observation. The theoretical model is used to represent the complex dynamics in the sea. Taking into account all the channel properties below the sea, the channel model was created. The AUVs are connected to a cluster-based network and the 3-D location of the AUVs is transmitted using an appropriate depth-based cluster-based routing protocol (DB-CBRP). Through choosing the optimal frequency for the transmission of routing packets, the network's total life is extended with the least delay in routing. As a result of its strength against overly reduced transmission capability and recurrence reuse, the CBRP approach is used to restrict channel impairments. The simulation results of the proposed algorithm reveal that the surveillance AUVs have greater communication, coverage and share their position with each other.

Real-time in situ prediction of ocean currents

The prediction of ocean currents is essential for the path planning and control of Autonomous Underwater Vehicles. Regional physics-based forecast models provide valid predictions but are too computationally expensive for real-time prediction necessary for AUV navigation. While vehicle sensors can measure the spatial evolution of currents, temporal prediction remains an open problem as existing data-driven models with real-time capabilities have only been shown to work at locations where data have been used to develop the model. We propose in this paper two predictive tools using deep learning techniques, a Long Short-Term Memory (LSTM) Recurrent Neural Network and a Transformer, to perform real-time in-situ prediction of ocean currents at any location. A data set from the National Oceanic and Atmospheric Administration is split in two distinct sets to train and test the models. We show that the LSTM and the Transformer have an averaged Normalized Root Mean Squared Error respectively of 0.10 and 0.11 over all test sites with a standard deviation respectively of 0.024 and 0.031. Comparisons with Harmonic Method predictions at various locations in the territorial sea of the United States show that both models provide state-of-the-art accuracy without having been trained with data from these sites.

Controller design for underwater robotic vehicle based on improved whale optimization algorithm

This paper presents the impact of introducing a two-controller on the linearized autonomous underwater vehicle (AUV) for vertical motion control. These controllers are presented to overcome the sensor noise of the AUV control model that effect on the tolerance and stability of the depth motion control. Linear quadratic Gaussian (LQG) controller is cascaded with AUV model to adapt the tolerance and the stability of the system and compared with FOPID established by the improved whale optimization algorithm (IWOA) to identify which controlling method can make the system more harmonize and tolerable. The developed algorithm is based on improving the original WOA by reshaping a specific detail on WOA to earn a warranty that the new IWOA will have values for the update position lower than the identified lower-bound (LB), and upper-bound (UB). Furthermore, the algorithm is examined by a set of test functions that include (unimodal, multimodal and fixed dimension multimodal functions). The privileges of applying IWOA are reducing the executing time and obtaining the semi-optimal objective function as compared with the original WOA algorithm and other popular swarm-intelligence optimization algorithms.

A Review of the Path Planning and Formation Control for Multiple Autonomous Underwater Vehicles

Path planning and formation control are two of the most significant concepts which can be considered in multi-vehicle systems and particularly in autonomous underwater vehicles (AUVs). The cooperative implementation of complicated commands would lead to desirable results and increase the probability of success in the missions. Due to the nonlinear dynamics and environmental conditions, the cooperative control of AUVs is a challenging topic. The developments in AUV applications demonstrate the significance of research and development in path planning and formation control. Unlike ground or aerial autonomous vehicles, this field of study has not attracted considerable attention and further exploration is required as a result. The present paper reviews the different structures of formation control in AUVs and discusses their advantages and disadvantages. Besides formation control, the cooperative path planning of AUVs along with the limitations specific to the cooperative structure is taken into consideration in the present study. Moreover, avoiding any obstacle collision and preventing any encounter between group members are considered as critical issues in the formation control and cooperative path planning. Some areas are still open to investigation as implied by the technological suggestions, which will facilitate future research. At the end of the article, a simulated sample is given of the triangular formation path planning for AUVs.

3-Dimensional Modeling and Attitude Control of Multi-Joint Autonomous Underwater Vehicles

To achieve rapid and flexible vertical profile exploration of deep-sea hybrid structures, a multi-joint autonomous underwater vehicle (MJ-AUV) with orthogonal joints was designed. This paper focuses on the 3-dimensional (3D) modeling and attitude control of the designed vehicle. Considering the situation of gravity and buoyancy imbalance, a 3D model of the MJ-AUV was established according to Newton’s second law and torque balance principle. And then the numerical simulation was carried out to verify the credibility of the model. To solve the problems that the pitch and yaw attitude of the MJ-AUV are coupled and the disturbance is unknown, a linear quadratic regulator (LQR) decoupling control method based on a linear extended state observer (LESO) was proposed. The system was decoupled into pitch and yaw subsystems, treated the internal forces and external disturbances of each subsystem as total disturbances, and estimated the total disturbances with LESO. The control law was divided into two parts. The first part was the total disturbance compensator, while the second part was the linear state feedback controller. The simulation results show that the overshoot of the controlled system in the dynamic process is nearly 0 rad, reaching the design value very smoothly. Moreover, when the controlled system is in a stable state, the control precision is within 0.005%.

Robust platoon control of underactuated autonomous underwater vehicles subjected to nonlinearities, uncertainties and range and angle constraints

This paper studies the three-dimensional platoon control of multiple underactuated autonomous underwater vehicles (AUVs) subjected to environmental disturbances and model uncertainties. The main control objective is to design a tracking controller to force a platoon of AUVs to construct a convoy-like formation along feasible trajectories while each consecutive pair preserves a desired line-of-sight distance with limited communication range and every possible collision is avoided between consecutive vehicles. To achieve this objective, the prescribed performance function (PPF) methodology is employed to constrain the relative distance and angles between successive pairs during their motion. A robust neural network (NN), hyperbolic tangent function, and dynamic surface control technique are simultaneously utilized to propose the prescribed performance-based controller which is robust against completely unknown parameters, nonlinear hydrodynamic damping, actuators saturation nonlinearity, NN approximation errors and time-varying environmental disturbances. Lyapunov's direct method is used to prove that all signals of the closed-loop control system are bounded and the relative distance and angles converge to a neighbourhood of the origin. Finally, numerical computer simulations verify the proposed controller performance for offshore applications.

Virtual Guidance-Based Coordinated Tracking Control of Multi-Autonomous Underwater Vehicles Using Composite Neural Learning.

This article proposes a virtual leader-based coordinated controller for the nonlinear multiple autonomous underwater vehicles (multi-AUVs) with the system uncertainties. To achieve the coordinated formation, a virtual AUV is set as the leader, while the desired command is designed using the relative position between each AUV and the virtual leader. The controller is designed based on the back-stepping scheme, and the online data-based learning scheme is used for uncertainty approximation. The highlight is that compared with previous learning methods which mostly focus on stability, the learning performance index is constructed using the collected online data in this article. The index is further used in the composite update law of the neural weights. The closed-loop system stability is analyzed via the Lyapunov approach. The simulation test on the five AUVs under fixed formation shows that the proposed method can achieve higher tracking performance with improved approximation accuracy.

Two-Loop System with Reference Model for Control of Spatial Movement of Cargo Underwater Vehicle

Currently, autonomous underwater vehicles (AUV) are increasingly used to perform tasks related to the maintenance of underwater communications and various underwater production complexes, as well as performing underwater technological operations. To effectively perform these operations, AUV must have high-quality control systems that will ensure their accurate movement both along long spatial trajectories formed during their movement to the objects of work, and when performing complex maneuvers near underwater infrastructure objects. At the same time, the main difficulty that arises in the process of synthesis of AUV control systems is the significant non-linearity of the dynamic models of these control objects, the presence of interactions between their degrees of freedom, as well as the uncertainty and variability of their parameters. In this paper, we propose a method for synthesizing the spatial motion control system of the AUV, which allows us to take into account these negative effects. This system contains two loops. The first loop includes a combined system containing a nonlinear controller to achieve the desired dynamic characteristics of the AUV, when its parameters are equal to the nominal values, and a controller with self-tuning according to the reference model, which provides compensation for an unknown or variable part of the parameters. In this case, the parameters of the controller with the reference model are selected to reduce the possible amplitude of the discontinuous signal for controlling the AUV velocity. The second loop is a non-linear position controller that allows to take into account the dynamic properties of the velocity control loop and the kinematic properties of the AUV. The advantage of the proposed control system in comparison with traditional ones based on PID controllers is a higher control accuracy when moving along complex spatial trajectories, regardless of changes in the AUV parameters. The simulation results confirmed the high efficiency of the synthesized two-loop control system.

Adaptive terminal sliding mode control of autonomous underwater vehicle (AUV) depth based on a disturbance observer

Adaptive terminal sliding mode control of autonomous underwater vehicle (AUV) depth based on a disturbance observer