Dynamics Of Autonomous Underwater Vehicle Research Articles

Robust Control of Formation Dynamics for Autonomous Underwater Vehicles in Horizontal Plane

This paper presents a novel robust controller design for formation control of autonomous underwater vehicles (AUVs). We consider a nonlinear three-degree-of-freedom dynamic model for the horizontal motion of each AUV. By using the Jacobi transform, the horizontal dynamics of AUVs are explicitly expressed as dynamics for formation shape and formation center, and are further decoupled by feedback control. We treat the coupling terms as perturbations to the decoupled system. An H-infinity state feedback controller is designed to achieve robust stability of the closed loop formation and translation dynamics. By incorporating an orientation controller, the formation shape under control converges and the formation center tracks a desired trajectory simultaneously. Simulation results demonstrate the effectiveness of the controllers.

An agent to optimally re-distribute control in an underactuated AUV

A knowledge-based agent was designed and validated to optimally re-distribute control authority in a torpedo-shaped autonomous underwater vehicle (AUV). The objective is greater fault tolerance in AUVs on long deployments when an AUV is unexpectedly underactuated from a jammed control fin. The optimisation is achieved through a genetic algorithm (GA) that evaluates solutions based on a full non-linear analysis of the AUV dynamics and control. The AUV dynamics, hydrodynamics, and control have to be well known ahead of time. The agent is implemented on-board the AUV to provide timely re-assignment of the fin control authority (gains), underway, and consequently the mission can continue or a potential vehicle loss averted. The effectiveness of the agent is assessed through a parametric analysis that compares the response of the unexpectedly underactuated AUV with its initial gains against the optimised gains. The agent’s greatest impact is in the event of a bow fin jam as the remaining three planes cannot depth-keep well without the agent.

A time differences of arrival‐based homing strategy for autonomous underwater vehicles

AbstractA new sensor‐based homing integrated guidance and control law is presented to drive an underactuated autonomous underwater vehicle (AUV) toward a fixed target, in 3‐D, using the information provided by an ultra‐short baseline (USBL) positioning system. The guidance and control law is first derived at a kinematic level, expressed on the space of the time differences of arrival (TDOAs), as directly measured by the USBL sensor, and assuming the plane wave approximation. Afterwards, the control law is extended for the dynamics of an underactuated AUV resorting to backstepping techniques. The proposed Lyapunov‐based control law yields almost global asymptotic stability (AGAS) in the absence of external disturbances and is further extended, keeping the same properties, to the case where known ocean currents affect the motion of the vehicle. Simulations are presented and discussed that illustrate the performance and behavior of the overall closed‐loop system in the presence of realistic sensor measurements and actuator saturation. Copyright © 2009 John Wiley & Sons, Ltd.

Transformed equations of motion for underwater vehicles

A method for dynamics investigation and coupling detection between velocities of autonomous underwater vehicles (AUVs) is presented in this paper. The method is based on transformation of equations of motion, which are usually used for an underwater vehicle, into equations with a diagonal mass matrix. The obtained equations contain quasi-velocities and allow one to give a further insight into the AUV dynamics especially for an underactuated system. Some advantages of the proposed approach are discussed, too. An analytical example for a 3-DOF AUV shows possible application of the transformed equations. Moreover, the given approach is validated via simulation on a 6-DOF vehicle.

Discrete time-delay control of an autonomous underwater vehicle: Theory and experimental results

A discrete time-delay control (DTDC) law for a general six degrees of freedom unsymmetric autonomous underwater vehicle (AUV) is presented. Hydrodynamic parameters like added mass coefficients and drag coefficients, which are generally uncertain, are not required by the controller. This control law cancels the uncertainties in the AUV dynamics by direct estimation of the uncertainties using time-delay estimation technique. The discrete-time version of the time-delay control does not require the derivative of the system state to be measured or estimated, which is required by the continuous-time version of the controller. This particularly provides an advantage over continuous-time controller in terms of computational effort or availability of sensors for measuring state derivatives, i.e., linear and angular accelerations. Implementation issues for practical realization of the controller are discussed. Experiments on a test-bed AUV were conducted in depth, pitch, and yaw degrees of freedom. Results show that the proposed control law performs well in the presence of uncertainties.

$H_{2}$ and $H_{\infty}$ Designs for Diving and Course Control of an Autonomous Underwater Vehicle in Presence of Waves

H 2 and H infin designs applied to the diving and course control of an autonomous underwater vehicle (AUV) considering the presence of wave disturbances are described. The six-degrees-of-freedom equations of motion of the vehicle are described as a linear model and divided into three noninteracting (or lightly interacting) subsystems for speed control, steering, and diving. This work is based on the slender form of the Naval Postgraduate School (NPS, Monterey, CA) AUV, considering that the subsystems can be controlled by means of two single-screw propellers, a rudder, port and starboard bow planes, and a stern plane. A model of the AUV dynamics is presented with the first- and the second-order wave force disturbances, i.e., the Froude-Kriloff and diffraction forces. An algorithm of nonlinear regression for the rationalization of the subsurface sea spectrum is provided in this case study. The obtained results are analyzed and evaluated in the frequency domain comparing the controllers performance considering or not the inclusion of the model of waves.

Stable nonlinear adaptive controller for an autonomous underwater vehicle using neural networks

In general, the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions. For this reason, high performance control system for an AUV usually should have the capacities of learning and adaptation to the time-varying dynamics of the vehicle. In this article, we present a robust adaptive nonlinear control scheme for an AUV, where a linearly parameterized neural network (LPNN) is introduced to approximate the uncertainties of the vehicle's dynamics, and the basis function vector of the network is constructed according to the vehicle's physical properties. The proposed control scheme can guarantee that all of the signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.

Observer Kalman filter identification of an autonomous underwater vehicle

This paper deals with the identification of linear discrete-time multivariable models of an autonomous underwater vehicle (AUV). The observer Kalman filter identification (OKID) method is applied with the main objective of evaluating its effectiveness to the experimental identification of the dynamic behaviour of an AUV. After presenting the mathematical background of the OKID algorithm, the proposed method is first validated on the basis of simulated data of both the linearized and nonlinear yaw dynamics of an AUV. Subsequently, the identification algorithm is applied to a set of experimental data. Results suggest that the method can be an efficient tool for the experimental identification of AUV dynamics.

A neural network adaptive controller design for free-pitch-angle diving behavior of an autonomous underwater vehicle

This paper presents a neural network adaptive controller for diving control of an autonomous underwater vehicle (AUV). In general, while deriving the diving equations of an AUV, the pitch angle of the vehicle is often assumed to be small in the diving motion. This is a somewhat strong restricting condition in many practical applications, and would be broken in this paper. Furthermore, because the dynamics of AUVs are highly nonlinear and the hydrodynamic coefficients of the vehicles are difficult to be accurately estimated a priori, the smooth unknown dynamics in the pitch motion of an AUV is approximated by a neural network, and the remaining unstructured uncertainties, such as disturbances and unmodeled dynamics, are assumed to be unbounded, although they still satisfy certain growth conditions. Under a certain relaxed assumptions on the control gain functions, proposed control scheme can guarantee that all the signals in the closed-loop system satisfy to be uniformly ultimately bounded (UUB). Simulation studies are included to illustrate the effectiveness of the proposed control scheme, and some practical features of the control laws are also discussed.

Modeling and simulation of autonomous underwater vehicles: design and implementation

Autonomous underwater vehicles (AUVs) have many scientific, military, and commercial applications because of their potential capabilities and significant cost-performance improvements over traditional means for performing search and survey. The development of a reliable sampling platform requires a thorough system design and many costly at-sea trials during which systems specifications can be validated. Modeling and simulation provides a cost-effective measure to carry out preliminary component, system (hardware and software), and mission testing and verification, thereby reducing the number of potential failures in at-sea trials. An accurate simulation can help engineers to find hidden errors in the AUV embedded software and gain insights into the AUV operations and dynamics. This paper reviews our research work on real-time physics-based modeling and simulation for our AUVs. The modeling component includes vehicle dynamics, environment and sensor characteristics. The simulation component consists of stand-alone versus hardware-in-the-loop (HIL) implementation, for both single as well as multiple vehicles. In particular, implementation issues with regard to multitasking system resources will be addressed. The main contribution of this paper is to present the rationale for our simulation architecture and the lessons learned.

신경회로망을 이용한 자율무인잠수정의 적응제어

Since the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different vehicle's operating conditions, high performance control systems of AUVs are needed to have the capacities of teaming and adapting to the variations of the vehicle's dynamics. In this paper, a linearly parameterized neural network (LPNN) is used to approximate the uncertainties of the vehicle dynamics, where the basis function vector of the network is constructed according to the vehicle's physical properties. The network's reconstruction errors and the disturbances in the vehicle dynamics are assumed be bounded although the bound may be unknown. To attenuate this unknown bounded uncertainty, a certain estimation scheme for this unknown bound is introduced combined with a sliding mode scheme. The proposed controller is proven to guarantee that all signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.

ACOUSTIC TRACKING OF AUTONOMOUS UNDERWATER VEHICLES BY A SET-MEMBERSHIP APPROACH

An algorithm is presented for tracking of Autonomous Underwater Vehicles (AUVs) from acoustic time-of-flight measurements received by a field of surface floating buoys. The algorithm assumes that measurements and AUV dynamics uncertainties are unknown but bounded, with known bounds, and produces as output the set of admissible AUV positions. The algorithm has been validated by simulation in which uncertainty models have been obtained from field data at sea.

On-line learning control of autonomous underwater vehicles using feedforward neural networks

A neural-network-based learning control scheme for the motion control of autonomous underwater vehicles (AUV) is described. The scheme has a number of advantages over the classical control schemes and conventional adaptive control techniques. The dynamics of the controlled vehicle need not be fully known. The controller with the aid of a gain layer learns the dynamics and adapts fast to give the correct control action. The dynamic response and tracking performance could be accurately controlled by adjusting the network learning rate. A modified direct control scheme using multilayered neural network architecture is used in the studies with backpropagation as the learning algorithm. Results of simulation studies using nonlinear AUV dynamics are described in detail. The robustness of the control system to sudden and slow varying disturbances in the dynamics is studied and the results are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>