A comparative study of control techniques for an underwater flight vehicle

In this chapter, the receding horizon Kalman filter is applied to underwater navigation systems. The ocean covers about two-thirds of the earth and has a great effect on human beings. However, the ocean is overlooked while we focus our attention on land and atmospheric issues; we have not been able to explore the full depths of the ocean, its abundant livings and non-living resources. For example, only recently we have discovered, by using manned submersibles, that a large amount of methane and carbon dioxide comes from the seafloor and extraordinary groups of organisms live in hydrothermal vent areas. However, a number of complex issues due to the unstructured and hazardous undersea environment make it difficult to survey in the ocean even though today’s technologies have allowed humans to land on the moon and robots to travel to Mars. Unmanned underwater vehicles (UUVs) can help us better understand marine and other environmental issues, protect the ocean resources of the earth from pollution, and efficiently utilize them for human welfare. The UUV is a platform for a variety of sensors: acoustic, magnetic, gravimetric and chemical ones. Most commercial UUVs are tethered and remotely operated, referred to as remotely operated vehicles (ROVs). Extensive use of manned submersibles and ROVs are currently limited to a few applications because of very high operational costs, operator fatigue and safety issues. The demand for advanced underwater vehicle technologies is growing and will eventually lead to fully autonomous and reliable underwater vehicles. Autonomous underwater vehicles (AUVs) were initially developed to perform missions that were not easy for ROVs and manned underwater vehicles. Since the autonomy allows AUVs to be used for risky missions such as a mine countermeasure (MCM) or under-ice operations, AUVs are replacing ROVs towed vehicles as well as manned underwater vehicles (Whitcomb, 2000). For detailed ocean surveys, an AUV acts as a more stable platform for precision sensors than ROVs or towed vehicles because an AUV is not subject to physical disturbances transmitted along the cable to the surface vessel. This absence of physical attachment also allows AUVs to measure ocean characteristics at specific depths and perform bottom-following missions as owing to its autonomy. In short, An AUV provides marine researchers with a new form of access to deeper ocean. For an AUV to successfully complete a typical survey mission, it must follow a path specified by the operator as closely as possible and arrive at a precise location for collecting data. When an AUV is not able to follow the path accurately during the mission, critical Source: Kalman Filter, Book edited by: Vedran Kordic, ISBN 978-953-307-094-0, pp. 390, May 2010, INTECH, Croatia, downloaded from SCIYO.COM

As the continuity of Unmanned Underwater Vehicle (UUV) research, we have been developing the small size of underwater vehicle [1]. Commercially this vehicle was known with Remotely operated vehicle (ROV). This vehicle have dimension (LxWxH) 490mmx410mmx320mm with weight on air around 7 kg. Total buoyancy of this vehicle construction around 8 kg. So we still have spare 1 kg buoyancy for payload. The maximum operating depth of this vehicle designed at maximum 30 meters of Salt water. Main construction of this vehicle is from Composite that’s have strength enough to hold all external load and forces. This mini ROV categorized as small observation ROV that’s just have camera and underwater lamp to observe underwater site and also as main navigation tools for pilot to operate this vehicle. In order to propelled this vehicle, we have attached three brushless DC Electric underwater thruster with capacity 200 Watt. This vehicle controlled by pilot on surface station via umbilical cable. The communication between vehicle and surface station used RS-485 standard communication. Remotely operated underwater vehicles are progressively more used for routine inspection and maintenance tasks. The most application that we have designed before was for ship hull inspection. Marine growth and cathodic protection anode inspection is the main task for this ROV on ship hull Maintenance. But with the additional payload like small manipulator can increased the task that’s it can handle, likes collecting sample and cutting rope. Step by step design process until preliminary testing maneuver on pool will be described in this paper.

The use of underwater vehicles, either remotely operated vehicles (ROVs) or autonomous underwater vehicles (AUVs), has become increasingly relevant in the operation and maintenance (O&M) routines of offshore wind farms. This article provides a critical review of how these platforms are being integrated into inspection and maintenance tasks, contributing not only to safer and more precise operations but also to greater autonomy in challenging marine environments. Beyond the technical and operational aspects, this review highlights their growing connection with artificial intelligence, digital twins, and multi-robot collaboration. The studies analyzed indicate a progressive shift away from conventional methods, traditionally dependent on crewed vessels and manual inspections, toward more automated, sustainable, and integrated approaches that align with the environmental and social commitments of the offshore wind sector. Finally, emerging trends and persisting obstacles, notably energy autonomy, are discussed, outlining the requirements for consolidating a robust, connected, and sustainability-oriented model for offshore maintenance.

Nowadays, the increasing availability of commercial off-the-shelf underwater acoustic and non-acoustic (e.g., optical and electromagnetic) modems that can be employed for both short-range broadband and long-range low-rate communication, the increasing level of autonomy of underwater vehicles, and the refinement of their underwater navigation systems pave the way for several new applications, such as data muling from underwater sensor networks and the transmission of real-time video streams underwater. In addition, these new developments inspired many companies to start designing hybrid wireless-driven underwater vehicles specifically tailored for off-shore operations and that are able to behave either as remotely operated vehicles (ROVs) or as autonomous underwater vehicles (AUVs), depending on both the type of mission they are required to perform and the limitations imposed by underwater communication channels. In this paper, we evaluate the actual quality of service (QoS) achievable with an underwater wireless-piloted vehicle, addressing the realistic aspects found in the underwater domain, first reviewing the current state-of-the-art of communication technologies and then proposing the list of application streams needed for control of the underwater vehicle, grouping them in different working modes according to the level of autonomy required by the off-shore mission. The proposed system is finally evaluated by employing the DESERT Underwater simulation framework by specifically analyzing the QoS that can be provided to each application stream when using a multimodal underwater communication system specifically designed to support different traffic-based QoSs. Both the analysis and the results show that changes in the underwater environment have a strong impact on the range and on the stability of the communication link.

Review of EMC standards for Underwater Vehicles

The problem of designing an accurate and reliable control for an Autonomous Underwater Vehicle (AUV), which is being subjected to environmental disturbances as well as configuration related changes, is critical in order to accomplish a successful mission. Any real-world problem solving system must deal with the issue of uncertainty, since the system's knowledge of the world is always incomplete, imprecise, and uncertain. This situation is aggravated for an AUV, due to the complex oceanic environment, and the inevitable noise of the sensory system. Some major facts that contribute to the difficulty of the underwater vehicle control are: • the dynamic behavior of the vehicle is highly nonlinear, • hydrodynamic coefficients cannot be easily obtained, hence making up uncertainties in the model knowledge, • the vehicle main body can be disturbed due to the ocean currents and vehicle motion. Therefore, it is difficult to obtain high performance by using the conventional control strategies. The control system should be able to learn and adapt itself to the changes in the dynamics of the vehicle and its environment. Many control methods have been proposed by researchers during the last decade, and there still exists a trend towards finding a better control law to achieve exponential stability while accounting for environmental changes and vehicle uncertainties. Focusing on the low level motion control of AUVs, most of the proposed control schemes take into account the uncertainty in the model by resorting to an adaptive strategy ((Corradini & Orlando, 1997), (Fossen & Sagatun, 1991a) and (Narasimhan & Singh, 2006)), or a robust approach ((Marco & Healey, 2001) and (Healey & Lienard, 1993)). In (Healey & Lienard, 1993) an estimation of the dynamic parameters of the vehicle NPS AUV Phoenix is also provided. Other relevant works on the adaptive and robust control of underwater vehicles are (Cristi & Healey, 1989), and (Cristi et al., 1990). (Leonard & Krishnaprasad, 1994) considers the control of an AUV in the event of an actuator failure. Experimental results on underwater vehicle control have been addressed by many researchers (e.g. see (Antonelli et al., 1999), (Antonelli et al., 2001), and (Zhao & Yuh, 2005)). An overview of control techniques for AUVs is reported in (Fossen, 1994). The aim of this chapter is to design a control system that would achieve perfect tracking for all configuration variables (e.g. sway and yaw motions) for any desired trajectory. To this end, we present the application of nonlinear control methods to an AUV that would lead to O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

A tethered flight vehicle such as Subzero III can be a reliable test-bed for AUV control techniques provided that the tether effects be removed by feed-forward control. To achieve the composite control idea, a numerical scheme for prediction of tether effects has been proposed and assessed. We argue that the composite control scheme can also be applied to the control of an ROV during the deployment of the umbilical. The H∞ approach has been applied to the design of the autopilots for autonomous underwater vehicles. The autopilots developed have the following features: (1) they have simpler structures than conventional H∞ design and the performance degrada tion caused by the controller order reduction is negligible; (2) rate feedback is applied for heading and depth control to improve tracking performance; (3) the overshoot specification is considered in the design by choosing suitable weighting functions. The results of both nonlinear simulations and water trials in two different tanks show that the autopilots are robust to model uncertainty, external disturbances and varying dynamics of the vehicle and exhibit good tracking performance. Thus the effectiveness of robust control for AUVs appears promising.

Autonomous underwater vehicles (AUVs) have been widely used to perform underwater tasks. Due to the environmental disturbances, underactuated problems, system constraints, and system coupling, AUV trajectory tracking control is challenging. Thus, further investigation of dynamic characteristics and trajectory tracking control methods of the AUV motion system will be of great importance to improve underwater task performance. An AUV controller must be able to cope with various challenges with the underwater vehicle, adaptively update the reference model, and overcome unexpected deviations. In order to identify modeling strategies and the best control practices, this paper presents an overview of the main factors of control-oriented models and control strategies for AUVs. In modeling, two fields are considered: (i) models that come from simplifications of Fossen’s equations; and (ii) system identification models. For each category, a brief description of the control-oriented modeling strategies is given. In the control field, three relevant aspects are considered: (i) significance of AUV trajectory tracking control, (ii) control strategies; and (iii) control performance. For each aspect, the most important features are explained. Furthermore, in the aspect of control strategies, mathematical modeling study and physical experiment study are introduced in detail. Finally, with the aim of establishing the acceptability of the reported modeling and control techniques, as well as challenges that remain open, a discussion and a case study are presented. The literature review shows the development of new control-oriented models, the research in the estimation of unknown inputs, and the development of more innovative control strategies for AUV trajectory tracking systems are still open problems that must be addressed in the short term.

AUV position tracking and trajectory control based on fast-deployed deep reinforcement learning method

In the underwater environment, robust control algorithms are required to control autonomous underwater vehicles (AUVs) at high speed while preventing large nonlinearities and disturbances. Sliding mode control (SMC) is a well-known robust control theory and has been widely used not only in AUV control but also in systems such as industrial robots which have high nonlinearity in their system dynamics. However, SMC has the disadvantage of causing chattering on the control input, and it is difficult to apply this method to the control fins of an AUV system that cannot move its fins at high speed underwater. In this work, a design for a sliding mode control with sliding perturbation observer (SMCSPO) algorithm is applied to AUVs, and the simulation results under underwater disturbance conditions are discussed. From simulation using MATLAB, it is confirmed that AUV control using SMCSPO shows better trajectory tracking control performance without chattering than PID control.

This paper deals with the control of underactuated autonomous underwater vehicles (AUVs). AUVs are needed in many applications such as the exploration of oceans, scientific and military missions, etc. There are many challenges in the control of AUVs due to the complexity of the AUV model, the unmodelled dynamics, the uncertainties and the environmental disturbances. A trajectory tracking control scheme is proposed in this paper; this control scheme is designed using the sliding mode control technique in order to be robust against bounded disturbances. The control performance of an example AUV, using the proposed method, is evaluated through computer simulations. These simulation studies, which consider different reference trajectories, show that the proposed control scheme is robust under bounded disturbances.

Non-parametric system identification with Gaussian Processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle (AUV) dynamics with low amount of data. Multi-output Gaussian processes and its aptitude to model the dynamic system of an underactuated AUV without losing the relationships between tied outputs is used. The simulation of a first-principles model of a Remus 100 AUV is employed to capture data for the training and validation of the multi-output Gaussian processes. The metric and required procedure to carry out multi-output Gaussian processes for AUV with 6 degrees of freedom (DoF) is also shown in this paper. Multi-output Gaussian processes are compared with the popular technique of recurrent neural network show that Multi-output Gaussian processes manage to surpass RNN for non-parametric dynamic system identification in underwater vehicles with highly coupled DoF with the added benefit of providing a measurement of confidence.

The development of advanced acoustic and optical imaging techniques along with autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) has enabled high-resolution benthic mapping and biological characterization of mesophotic coral ecosystems (MCEs) over large spatial scales. Underwater vehicles can be grouped into tethered and untethered vehicles. Tethered vehicles allow the pilot on the ship to remotely control the vehicle using live video streaming. Tethered vehicles include ROVs, towed sleds, and drop cameras. Untethered vehicles, particularly AUVs, rely on autonomy for navigation, obstacle avoidance, and situational awareness. Untethered vehicles also include manned submersibles and Lagrangian buoys. Hybrid remotely operated vehicles combine the capabilities of both AUVs and ROVs into one vehicle. Acoustic mapping using side-scan or multibeam sonars can offer much broader coverage than optical imaging and can be useful for habitat mapping. However, acoustic data generally have lower spatial resolutions and are more difficult to interpret since similar acoustic returns can correspond to more than one bottom type. Optical imaging from still and video cameras at close range delivers high-resolution data that are easy to interpret. Increased precision in underwater navigation enables time series of georeferenced optical and acoustical imagery for fine-scale detection of changes over longer temporal scales of years to decades. This chapter provides a summary of the advantages and disadvantages of both tethered and untethered vehicles for studies of MCEs, including their various imaging modalities, as well as illumination, positioning, navigation, and imaging considerations.

摘要: 基于主-从系统状态同步的思想，提出了母艇在平面运动中回收自治水下航行器（Autonomous underwater vehicle，AUV）的一种控制方法. 在给出母艇和自治水下航行器的动力学模型基础上，建立了自治水下航行器（从系统）接收母艇（主系统）的状态信息并控制自身接近母艇的主从控制方案，使母艇自主回收水下航行器的问题转化为两者的运动状态同步问题. 利用有限时间稳定性理论，设计了一种在常值海流扰动影响下，自治水下航行器能够在有限时间内被母艇回收的滑模控制器，理论证明和仿真实例证实了该控制器的有效性. 关键词: 自治水下航行器 / 回收技术 / 有限时间同步 / 滑模控制

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper OTC 28312, “Understanding the Global Scientific Value of Industry ROV Data To Quantify Marine Ecology and Guide Offshore Decommissioning Strategies,” by D.L. McLean, University of Western Australia; P. Macreadie, Deakin University; D.J. White, University of Southampton and University of Western Australia; P.G. Thomson, University of Western Australia; A. Fowler, New South Wales Department of Primary Industries; A.R. Gates, National Oceanography Centre, UK; M. Benfield, Louisiana State University; T. Horton, National Oceanography Centre; D. Skropeta, University of Wollongong; T. Bond, University of Western Australia; D.J. Booth, University of Technology, Australia; E. Techera, C. Pattiaratchi, and S.P. Collin, University of Western Australia; D.O.B. Jones, National Oceanography Centre; L. Smith, Woodside Energy; and J.C. Partridge, University of Western Australia, prepared for the 2018 Offshore Technology Conference Asia, 20–23 March, Kuala Lumpur. The paper has not been peer reviewed. Copyright 2018 Offshore Technology Conference. Reproduced by permission. Remotely operated vehicles (ROVs) are used by the offshore oil and gas industry to monitor the integrity of subsea infrastructure and, in doing so, collect terabytes of video and in-situ physical data from inaccessible regions and poorly understood marine environments. The complete paper describes the potential global scientific value of video and other data collected•by ROVs. ROVs and the Offshore Oil and Gas Industry Nearly one-third of global production of oil and gas is sourced offshore. Tens of thousands of offshore wells have been drilled, and more than 900 large-scale offshore oil and gas platforms have been installed. The offshore industry relies on underwater observation, control, and intervention, tasks performed increasingly by ROVs; over 700 were in operation globally at the time the paper was written. The advancement of ROV technology for the industry was prompted in the 1970s by developments extending into water depths beyond the capabilities of divers, and by the desire to reduce reliance on diving operations. Modern ROVs fall into two main classes: work class and observation class. A work-class ROV is car-sized, usually cuboidal, with a buoyant upper part containing power equipment, thrusters, manipulators, and cameras. An umbilical connection to the surface vessel provides electrical and fiber-optic connections that allow control and data transfer. Observation-class ROVs are smaller—sometimes briefcase-sized—with limited power and manipulation capabilities, and are used primarily for inspection purposes. For observation missions that can be conducted without real-time intervention, autonomous underwater vehicles (AUVs) are increasingly popular. These are generally torpedo-shaped, with higher hydrodynamic stability than ROVs, and can offer a similar image-gathering platform. Oil and gas projects and other ocean-based industries such as offshore wind use ROVs at multiple stages in a project’s life. Surveys of the region before installation or construction are conducted using ROVs or AUVs to identify the bathymetry and substrate conditions for hazard assessments and to provide parameters for engineering design. Drilling activities commonly involve ROV monitoring at the seabed level, and, when the production facilities are installed, ROVs are used to perform observation of all subsea activities. During operation of a project, ROVs are used for inspection and intervention. The manipulators operate valves and interchange components, and, if required, can undertake cutting. Inspections include visual surveys as well as checking of anode electropotentials and clearance of marine fouling. Inspections commonly are undertaken annually but can be more or less frequent depending on risk level and maintenance requirements. In total, the ocean around a typical oil and gas development may be visited dozens of times by ROVs during the project’s life.