Estimation of AUV Hydrodynamic Coefficients Using Analytical and System Identification Approaches

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

Unmanned, underwater vehicles have been developed considerably in recent years. Remotely operated vehicles (ROVs) are increasingly used for routine inspection and maintenance tasks but have a range that is limited by the umbilical cable. For long range operations, such as oceanographic exploration and surveying, autonomous underwater vehicles (AUVs) are emerging which haveon-board power and are equipped with advanced control capabilities to carry out tasks with the minimum of human intervention. AUVs typically resemble torpedoes in that mosthave control surfaces and a single propulsion unit, and must move forwards to manoeuvre. Such vehicles are called flight vehicles. This paper describes techniques which are candidates for control of a flight AUV and identifies controllers used on some existing vehicles. Since underwater vehicle dynamics are nonlinear, fuzzy logic and sliding mode control were felt to have promise for autopilot application due to their potential robustness. Following development using a comprehensive simulation programme, the controllers were tested using the experimental vehicle, Subzero II, and their performance compared with that of a classical linear controller. The relative merits of the methods for practical implementation are discussed.

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

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.

In this paper, a new approach to stability analysis of nonlinear dynamics of an underactuated autonomous underwater vehicle (AUV) is presented. AUV is a highly nonlinear robotic system whose dynamic model includes coupled terms due to the hydrodynamic damping factors. It is difficult to analyze the stability of a nonlinear dynamical system through Routh's stability approach because it contains nonlinear dynamic parameters owing to hydrodynamic damping coefficients. It is also difficult to analyze the stability of AUVs using Lyapunov's criterion and LaSalle's invariance principle. In this paper, we proposed the extended-Routh's stability approach to verify the stability of such nonlinear dynamic systems. This extended-Routh's stability approach is much easier as compared to the other existing methods. Numerical simulations are presented to demonstrate the efficacy of the proposed stability verification of the nonlinear dynamic systems, e.g., an AUV system dynamics.

High-precision, limited-beacon-aided AUV localization algorithm

This paper presents a robust adaptive controller for autonomous diving control of an autonomous underwater vehicle (AUV). It is well known that the dynamics of underwater robotics vehicles (URVs) are highly nonlinear and the hydrodynamic coefficients of vehicles are difficult to be accurately determined a priori. In this paper, the smooth unknown dynamics of vehicle is approximated by neural network, and the remaining unstructured uncertainties, such as disturbances and unmodeled dynamics, are assumed to satisfy certain growth conditions characterized by 'bounding function' composed of known function multiplied by unknown constant. Under a certain relaxed assumptions on the control gain function, the proposed control scheme can guarantee that all the signals in the closed-loop system satisfy to be uniformly ultimately bounded (UUB). Simulation studies using ASUM AUV model, which is developing at KRISO, are included to illustrate the effectiveness of the proposed control scheme. Some practical features of the control laws are also discussed.

Having an accurate mathematical model is essential in design, control, and navigation process of an autonomous underwater vehicle (AUV). Due to the modeling simplifications, the available mathematical models suffer from the uncertainty of their parameters and they usually need an identification phase for improving the modeling accuracy. In case of AUVs, the hydrodynamic coefficients of the model play an important role and they should be identified using the available experimental data. In this paper, a 6-DoF dynamic model of an AUV is presented and then some of its parameters including viscous damping, the body lift, and control input coefficients that have the highest effects on the modeling error are identified by augmented state model method. The extended Kalman filter (EKF), cubature Kalman filter (CKF), and transformed unscented Kalman filter (TUKF) are used as the estimation filters. To verify and compare the three estimation filters with consideration of the predetermined hydrodynamic coefficients and spiral maneuver AUV results, these three methods are evaluated. The results indicate that the TUKF identifies the best hydrodynamic model due to solving both the CKF nonlocal sampling problem and the EKF linearization problem.

Models of the open‐loop dynamics for prototype Remotely Operated Underwater Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs) are required for performance prediction and autopilot design. This article describes techniques which can be used to derive these models. The methods may be conveniently grouped into predictive techniques i.e., those which require only vehicle design data and which predict dynamics before the prototype is built and testing techniques i.e., methods which measure open‐loop dynamics from the trials performance of a prototype ROV. The relative benefits and drawbacks of the various methods are examined.

Underwater navigation methodology based on intelligent velocity model for standard AUV

Hybrid model navigation method for autonomous underwater vehicle

In this paper, we propose a novel navigation method for underwater vehicles based on a single seafloor station (SS), with which the vehicles can estimate their positions and orientations with respect to the SS without the need of expensive inertial navigation system or time-consuming calibration. This method is suitable for near-seafloor applications requiring real-time and accurate positioning, such as seafloor imaging and sampling. The method is also suitable for autonomous underwater vehicles (AUVs) since no other external aid is necessary other than SS. The key idea is to utilize mutual acoustical measurements between the vehicle and the SS. Simply explained: 1) the vehicle starts by interrogating the SS acoustically and measures the range between the two units as well as the bearing of the SS with respect to the vehicle in the vehicle reference frame; and then, 2) the SS computes the bearing of the vehicle with respect to the SS and transmits this information back to the vehicle using a similar acoustical device. By combining this information and inputting it into a nonlinear filter structure that includes measurements of the vehicle's ground velocity and yaw angular velocity, the vehicle computes its position and heading estimates. A pair of acoustical devices named acoustical localization and communication (ALOC) devices that can communicate and calculate their relative positions have been built. Sea trials were carried out in October 2011 using the AUV Tri-Dog1 (TD) and a trial SS at Kagoshima Bay in Japan. The AUV successfully navigated around the SS based on the measurements of the ALOC device mounted on both the AUV and the SS. The performance of the method was verified through simulations based on the experimental results.

The rapid development of Autonomous Underwater Vehicles (AUVs) has increased the demand for propulsion systems that balance thrust density, hydrodynamic efficiency, and acoustic discretion. This study presents a comprehensive numerical investigation of the performance of the Blue Robotics T500 thruster, embedded within the RAS-HA-X25 AUV’s internal conduit. Using transient Computational Fluid Dynamics (CFD) within the OpenFOAM framework, this research assesses the propulsive characteristics of the thruster across six distinct outlet geometries, including convergent jet nozzles and multi-lobed “daisy” configurations. To improve computational efficiency for parametric design, a calibrated actuator disc model was developed and validated against resolved-rotor simulations, revealing a 15% discrepancy attributed to tip leakage and hub vortex effects. Results show that at the operational advance ratio (J=0.167), the 60 mm convergent nozzle is the optimal configuration for maximising thrust, achieving a peak net thrust of 42 N. In contrast, the daisy-type lobed geometries, while causing a 50% reduction in absolute thrust compared to a standard cylindrical pipe, significantly homogenise the exit-plane velocity distribution and reduce swirl intensity. These findings indicate that lobed terminations provide a viable mechanism for reducing hydroacoustic signatures, offering a strategic “stealth” advantage for low-observable underwater platforms where acoustic discretion is prioritised over pure thrust density. This study establishes a robust methodology for optimising embedded propulsion modules in next-generation autonomous and hybrid underwater vehicles.

The absence of global positioning system (GPS) signals and the influence of ocean currents are two of the main challenges facing the autonomy of autonomous underwater vehicles (AUVs). This paper proposes an acoustic localization-based tracking control method for AUVs. Particularly, three buoys that emit acoustic signals periodically are deployed over the surface. Times of arrivals of these acoustic signals at the AUV are then obtained and used to calculate an estimated position of the AUV. Moreover, the uncertainties involved in the localization and ocean currents are handled together in the framework of the extended Kalman filter. To deal with system physical constraints, model predictive control relying on online repetitive optimizations is applied in the tracking controller design. Furthermore, due to the different sampling times between localization and control, the dead-reckoning technique is utilized considering detailed AUV dynamics. To avoid using the highly nonlinear and complicated AUV dynamics in the online optimizations, successive linearizations are employed to achieve a trade-off between computational complexity and control performance. Simulation results show that the proposed algorithms are effective and can achieve the AUV tracking control goals.

To improve the observation capability of the ocean, the combination of the static cabled ocean observatory network and the dynamic autonomous underwater vehicle has attracted more and more attention. In this paper, a non-contact docking system for the autonomous underwater vehicle is developed to combine the advantages of the cabled ocean observatory network and the autonomous underwater vehicle. The system includes both acoustic and optical navigation, underwater wireless communication, non-contact power transfer, and monitoring and controlling of the docking system. This docking system was verified by sea trials at depths of 50 and 105 m. The autonomous underwater vehicle successfully docked 11 times, during which non-contact power transfer and wireless communication were completed. The charging power reached 682 W, with a total efficiency of 78.5%, and the efficiency of the power transfer unit was 92%. The rate of wireless data transfer reached 3.1 MB/s. For the first time, this docking system has realized complete homing and docking navigation, wireless communication and charging in a water depth of more than 100 m, thus establishing a good foundation for the combination of the cabled ocean observatory network and autonomous underwater vehicles.