Control System Of Autonomous Underwater Vehicle Research Articles

An Adaptive Hierarchical Sliding Mode Controller for Autonomous Underwater Vehicles

The paper addresses a problem of efficiently controlling an autonomous underwater vehicle (AUV), where its typical underactuated model is considered. Due to critical uncertainties and nonlinearities in the system caused by unavoidable external disturbances such as ocean currents when it operates, it is paramount to robustly maintain motions of the vehicle over time as expected. Therefore, it is proposed to employ the hierarchical sliding mode control technique to design the closed-loop control scheme for the device. However, exactly determining parameters of the AUV control system is impractical since its nonlinearities and external disturbances can vary those parameters over time. Thus, it is proposed to exploit neural networks to develop an adaptive learning mechanism that allows the system to learn its parameters adaptively. More importantly, stability of the AUV system controlled by the proposed approach is theoretically proved to be guaranteed by the use of the Lyapunov theory. Effectiveness of the proposed control scheme was verified by the experiments implemented in a synthetic environment, where the obtained results are highly promising.

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.

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.

Application of the computational fluid dynamics methods to obtain the characteristics of AUV transient responses

The purpose of this work is to create the most accurate mathematical model of the underwater vehicle dynamics. In fact, the proposed model should be an alternative to full-scale testing of the device. The paper presents a calculation method that implements coupled calculations of the underwater vehicle dynamics and the hydrodynamics of the fluid, flowing around it. From the point of view of mechanics and hydrodynamics, this approach is the most accurate method for modeling the device dynamics in the presence of arbitrary control actions. The main advantage of the proposed calculation method is the conservative approximation scheme for hydrodynamic calculations, which is extremely important when performing non-stationary calculations. In addition, the proposed method requires less computational resources than other currently used coupled calculations methods. The proposed method was verified on a large data volume received from real autonomous underwater vehicles (AUV) field tests and showed high accuracy in reproducing full-scale data. The developed calculation method was used for the designing AUV control system and showed its high efficiency.

Problematic Issues of Intellectualizing the Control System of Autonomous Underwater Vehicles

The paper considers ways of intellectualizing the control processes of autonomous underwater vehicles (AUVs) by the example of solving three problems, on which the successful use of AUVs largely depends. The first problem is to create an AUV control system (CS), which ensures the achievement of the mission’s objective in the event of emergencies caused by both external and internal reasons, as well as deliberate and unintentional counteraction. It is shown that for the construction of the AUV’s CS the decentralized multi-agent structure is most suitable, in which each AUV system is an independent intellectual agent with its own control system. The control system must be equipped with a set of adaptive algorithms that ensure: control of AUV in the event of emergency situations, taking into account restrictions on the supply of electric energy, speed, accuracy of autonomous underwater navigation, range of hydro-acoustic communication; rational distribution of energy resources by AUV systems in accordance with the current situation; maintaining the functional stability of the AUV with a partial malfunction of technical means. The second problem is to create an underwater navigation system that ensures the accomplishment of AUV missions at great distances from the base point. Since AUV navigation using only on-board means (inertial navigation system and lag) does not provide the necessary accuracy, a prerequisite for AUV navigation over long distances is to conduct an observation using external sources, the choice of which in the circumstances is a non-trivial task. The third problem is to create a network underwater communication system (NUCS), which provides for the group application of AUVs. The ground analogue of NUCS is network radio communication. But if the latter is fairly well developed, then the former only takes the initial steps. This is due to both the later practical relevance of NUCS, and many fundamental physical factors that impede the development of NUCS, which include: a substantially limited frequency band that can be used in practice for signal transmission; a large propagation time of the hydroacoustic signal compared to the radio signal; the formation of extended shadow zones and fading of the connected signal due to its multipath propagation; significant Doppler distortions, fast variability of the characteristics of the hydroacoustic medium.

Enhancing the roll dynamics of an AUV by contra-rotating-propellers

ABSTRACT Roll control of the Autonomous Underwater Vehicles (AUVs) is a significant issue in the current field of research for many researchers in the subject of AUV control system designation. Especially at higher speeds, the roll angle generated by a single rotating propeller or other external disturbances can considerably influence the whole dynamics and therefore the operation of the vehicle. In this paper, the utilisation of a system of contra-rotating-propellers (CRP) to enhance the roll dynamics of an AUV is evaluated by developing a six-degrees-of-freedom (6DOF) dynamics and control systems’ simulator. The results show that: 1. The single propeller system can cause roll angle deflections affecting the heading angle and depth of AUV depending on the forward speed. 2. Using the contra-rotating system with a proper roll controller can enhance the roll dynamics of the vehicle, particularly for the real-time roll stabilisation and maneuvering goals.

Autonomous Underwater Monitoring System for Detecting Life on the Seabed by Means of Computer Vision Cloud Services

Autonomous underwater vehicles (AUVs) have increasingly played a key role in monitoring the marine environment, studying its physical-chemical parameters for the supervision of endangered species. AUVs now include a power source and an intelligent control system that allows them to autonomously carry out programmed tasks. Their navigation system is much more challenging than that of land-based applications, due to the lack of connected networks in the marine environment. On the other hand, due to the latest developments in neural networks, particularly deep learning (DL), the visual recognition systems can achieve impressive performance. Computer vision (CV) has especially improved the field of object detection. Although all the developed DL algorithms can be deployed in the cloud, the present cloud computing system is unable to manage and analyze the massive amount of computing power and data. Edge intelligence is expected to replace DL computation in the cloud, providing various distributed, low-latency and reliable intelligent services. This paper proposes an AUV model system designed to overcome latency challenges in the supervision and tracking process by using edge computing in an IoT gateway. The IoT gateway is used to connect the AUV control system to the internet. The proposed model successfully carried out a long-term monitoring mission in a predefined area of shallow water in the Mar Menor (Spain) to track the underwater Pinna nobilis (fan mussel) species. The obtained results clearly justify the proposed system’s design and highlight the cloud and edge architecture performances. They also indicate the need for a hybrid cloud/edge architecture to ensure a real-time control loop for better latency and accuracy to meet the system’s requirements.

A Novel Network Scheduling Approach Based on Genetic Algorithm for Autonomous Underwater Vehicle Control

Autonomous Underwater Vehicle (AUV) is one type of important equipments for conducting ocean exploration missions. The AUV control system typically employs a common communication network to connect various function nodes such as Doppler meter, compass/inertial navigation unit, sonar, water depth sensor, main propulsion, and rudder motor. Each of these function nodes has its own information transmission requirements that need to be served by the limited bandwidth of the common communication network. In order to obtain a good control performance, an elaborate information transmission schedule in the communication network should be planned in such a way that all information transmission requirements in the AUV control system can be satisfied with as less network-induced delay and data packet dropout as possible. This paper proposes a novel network scheduling approach based on the Genetic Algorithm (GA) and the time-triggered architecture for meeting information transmission requirements in the Controller Area Network (CAN) of the AUV control system. The proposed network scheduling approach divides the whole transmission period into a serial of time slices with equal lengths, and defines a large cycle consisting of multiple time slices. For the periodical information transmission, the transmission schedule in one large cycle is determined by solving one nonlinear integer optimization problem with the Genetic Algorithm. This GA-based network scheduling approach maximizes the available bandwidth of each single time slice in all the periodic time slices of the whole schedule period, and aims to achieve a balanced information transmission in the CAN network. Meanwhile, the available bandwidth in each time slice of the whole schedule period is then allocated for the event-triggered information transmission. In this way, the proposed network scheduling approach satisfies the transmission requirements of both periodical and event-triggered information in the AUV. Finally, simulation experiments demonstrate the performance of the proposed GA-based network scheduling approach, and verify its performance improvement on an AUV heading motion control using TrueTime toolbox.

Fractional-Order PID Motion Control for AUV Using Cloud-Model-Based Quantum Genetic Algorithm

Aiming to improve the performance of motion for autonomous underwater vehicle (AUV), a fractional-order PID strategy is proposed. It is a more generalized form for the conventional integer-order PID controller, keeping its simplicity and utilizing the generalized derivative and integral control actions. The fractional-order PID controller has been successfully applied to heading control, diving control and path-following system of AUV on sea trial. In addition, the fractional-order closed-loop system has proven to be stable. By comparing simulations and experiments, the satisfactory performance, such as overshoot, settling time and steady-state error, has been achieved. The cloud-model-based quantum genetic algorithm (CQGA) is employed to tune coefficients of fractional-order PID controller. The quantum bits and quantum superposition states avoid the pressure of selection and maintain the diversity of population in chromosome coding. Due to the randomness and stability tendency of cloud droplets, the cloud crossover operator and the cloud mutation operator can effectively overcome the shortcomings of premature and slow searching speed. Numerical simulations show that the CQGA is more efficient to find the optimal coefficients of fractional-order PID controller than GA.

Point Stabilization for an Underactuated AUV in the Presence of Ocean Currents

This paper presents a state-feedback-based backstepping control algorithm to address the point stabilization (or set-point regulation) control problem for an underactuated autonomous underwater vehicle (AUV) in the presence of constant and irrotational ocean current disturbance. A nonlinear three degree of freedom dynamic model in the horizontal plane for an AUV without symmetry fore/aft is considered. The expression of the relationship between the desired heading angle of the AUV and direction angle of the ocean current, which is a necessary condition for precise point stabilization control of an underactuated AUV in the presence of ocean current disturbance is firstly discussed in this paper. The proposed backstepping control law for point stabilization has further been enriched by incorporating an additional integral action for enhancing the steady state performance of the AUV control system, while practical asymptotic stability analysis of the system is carried out using Lyapunov theory and Barbalat's Lemma. Simulation experiments of an underactuated AUV verify the theorem proposed and demonstrate the effectiveness of the controller.

Preliminary Numerical Study on Designing Navigation and Stability Control Systems for ITS AUV

In this paper, the numerical study of designing on navigation and stability control system for AUV is studied. The study started by initiating hydrostatic forces, added masses, lift force, drag forces and thrust forces. Determining the hydrodynamic force which is the basic need to know the numerical case study on designing on navigation and stability control system for AUV where Autonomous Underwater vehicles (AUV). AUV is capably underwater vehicle in moving automatically without direct control by humans according to the trajectory. The result of numerical study is properly to be the reference for the next developing for AUV.

An Autonomous Underwater Vehicle for Competition

The research of this paper was derived from the small autonomous underwater vehicle (AUV)Raider well performed in the 15th International Underwater Vehicle Competition (IAUVC),San Diego. In order to improve the performance of underwater vehicle, the control system of performance motion played an important role on autonomous underwater vehicles stable motion, and the whole control system of AUV is the main point. Firstly, based on the motion equations of six degrees of freedom, the paper simplified the dynamical model reasonably in allusion; Due to the speed of Raider to find the target was very low, this paper considered the speed was approximately zero and only considered the vertical motion. Therefore, this paper established the vertical hydrodynamic model of Raider, obtaining the transfer equation of vertical motion. Through the experiment and Matlab/Simulink simulation, this paper got the actual depth of the step response curve and simulation curve, and verified the validity of the vertical hydrodynamic model and the correlation coefficient.

A Simulation Environment for Deep-Sea Hydrothermal Plume Tracing with Autonomous Underwater Vehicles

For using an autonomous underwater vehicle(AUV) to localize seafloor hydrothermal vents fast and accurately, a biomimetic approach to hydrothermal plume tracing is proposed.And to support the investigation of AUV's biologicallyinspired control strategies,a graphical simulation environment is designed and developed.In this paper,hydrothermal plume tracing with AUVs and the modular architecture of the simulation environment are firstly introduced and described.Then,the modules of hydrothermal plume and flow field in simulation environment are modeled,and the efficient numerical solution algorithms are given.To facilitate Monte Carlo simulation,a set of stochastic initial and boundary conditions are set.And the modular behavior-based AUV control system architecture employed in the control system module is mainly described. The simulation environment addresses the key factors that complicate the investigation of AUV's biologically-inspired control strategies for the hydrothermal plume tracing,including the current field being non-uniform and non-steady,the plume distribution being irregular,intermittent and of large scale,the plume centreline being meandrous,and the plume containing buoyant part and nonconservative tracer.In addition,this simulation environment achieves good visual effect and high computational efficiency,which allows it to be suitable for real-time simulation and Monte Carlo simulation studies.The presented simulation environment provides sufficient support for the investigation of AUVs biologically-inspired control strategies for hydrothermal plume tracing.

Development of a power control system for AUVs probing for underwater mineral resources

Valuable mineral resources are widely distributed throughout the seabed. autonomous underwater vehicles (AUVs) are preferable to remotely-operated vehicles (ROVs) when probing for such mineral resources as the extensive exploration area makes it difficult to maintain contact with operators. AUVs depend on batteries, so their power consumption should be reduced to extend exploration time. Power for conventional marine instrument systems is incorporated in their waterproof sealing. External intermittent control of this power source until termination of exploration is challenging due to limitations imposed by the underwater environment. Thus, the AUV must have a power control system that can improve performance and maximize use of battery capacity. The authors developed such a power control system with a three-step algorithm. It automatically detects underwater operational states and can limit power, effectively decreasing power consumption by about 15%.

Design and evaluation of a hierarchical control architecture for an autonomous underwater vehicle

This paper researches on a kind of control architecture for autonomous underwater vehicle (AUV). After describing the hybrid property of the AUV control system, we present the hierarchical AUV control architecture. The architecture is organized in three layers: mission layer, task layer and execution layer. State supervisor and task coordinator are two key modules handling discrete events, so we describe these two modules in detail. Finally, we carried out a series of tests to verify this architecture. The test results show that the AUV can perform autonomous missions effectively and safely. We can conclude the control architecture is valid and practical.

An adaptive controller system using mnSOM

Underwater vehicles are expected to be attractive tools in the deep ocean. In order to realize useful and practical robots, underwater vehicles should take their action by judging the changing condition from their own sensors and actuators, and are desirable to make their behavior by themselves in the hazardous working environment. We have investigated the application of neural networks into the Autonomous Underwater Vehicles (AUVs). AUVs have complicated non-linear dynamics in six degrees of freedom. In our previous adaptive control method using NNs, the information of initial states is getting lost gradually during the process of adaptation. If the environment of the robot is changed, the former environmental information was not effectively reflected in our previous NNs controller. Therefore, the new method which keeps the information of initial state or previous environment and adapt to the new environment should be developed to increase the efficiency of learning and reduce the learning cost with the use of the former environmental information which the robot had learned. The new adaptive control system for AUV using modular network SOM by K. Tokunaga et al. has been proposed. The efficiency of the system is investigated through identification of dynamics of an underwater robot.

An intelligent system for failure detection and control in an autonomous underwater vehicle

Autonomous underwater vehicles (AUVs) have been used extensively in deep sea research. Failure detection and control is an important issue in maintaining the stability of an AUV. In most AUVs, the vehicle resurfaces in the event of minor failures such as in the depth sensor, the inclinometer, etc. The paper proposes an intelligent system for failure detection and control in AUVs where the vehicle could continue exploration in case of minor failures in the sensors and control surfaces. The intelligent system, based on the model proposed in Patel and Ranganathan (1996), integrates the adaptability of an artificial neural network (ANN) and the inferencing ability of a fuzzy rule based expert system on a single VLSI chip. The associative function of the ANN is used to recognize and detect the failures by observing the various changing parameters of the dynamic vehicle. The inferencing ability of an expert system suggests ways to control the failure and indicates the subsequent status of the vehicle. The entire system could be used as a low level diagnoser in an overall control system for AUVs.

Control architectures for autonomous underwater vehicles

Autonomous underwater vehicles (AUVs) share common control problems with other air, land, and water unmanned vehicles. In addition to requiring high-dimensional and computationally intensive sensory data for real-time mission execution, power and communication limitations in an underwater environment make it more difficult to develop a control architecture for an AUV. In this article, the four types of control architectures being used for AUVs (hierarchical, heterarchical, subsumption, and hybrid architecture) are reviewed. A summary of 25 existing AUVs and a review of 11 AUV control architecture systems present a flavor of the state of the art in AUV technology. A new sensor-based embedded AUV control system architecture is also described and its implementation is discussed.

An on-line adaptation method in a neural network based control system for AUVs

A neural network based control system Self-Organizing Neural-Net-Controller System: has been developed as an adaptive control system for Autonomous Underwater Vehicles (AUVs). In this paper, an on-line adaptation method is proposed to improve the time-consuming adaptation process of the original SONCS. The Imaginary Training can be realized by a parallel structure which enables the SONCS to adjust the controller network independently of actual operation of the controlled object. The SONCS is divided into two separate parts: the Real-World Part where the controlled object is operated according to the objective, and the Imaginary-World Part where the Imaginary Training is carried out. In order to adjust the controller network by the Imaginary Training, it is necessary to introduce a forward model network which can generate simulated state variables without involving actual data. A neural network Identification Network which has a specific structure to simulate the behavior of dynamical systems is proposed as the forward model network. The effectiveness of the Imaginary Training is demonstrated by applying to the heading keeping control of an AUV Twin-Burger. It is shown that the SONCS adjusts the controller network-through on-line processes in parallel with the actual operation. >