Control Of AUV Research Articles

Nonlinear Backstepping Control of Underactuated AUV in Diving Plane

Nonlinear Backstepping Control of Underactuated AUV in Diving Plane

Cooperative Cognitive Control for Autonomous Underwater Vehicles (CO3AUVs): overview and progresses in the 3rd project year

The paper describes the “Cooperative Cognitive Control for Autonomous Underwater Vehicles (Co3AUVs)” EU-project. This is a 7th Framework Program STREP project under the theme: Information and Communication Technologies Cognitive Systems, Interaction, Robotics. The project duration is February 2009 to January 2012. The aim of Co3AUVs is to develop, implement and test advanced cognitive systems for coordination and cooperative control of multiple AUVs. Several aspects are investigated including 3D perception and mapping, cooperative situation awareness, deliberation and navigation as well as behavioral control strictly linked with the underwater communication challenges. This paper presents an overview of the project with a focus on results from the final 3rd project year.

The effect of vehicle velocity and drift angle on through-body AUV tunnel thruster performance

New applications of streamlined Autonomous Underwater Vehicles require an AUV capable of completing missions with both high-speed straight-line runs and slow maneuvers or station keeping tasks. At low, or zero, forward speeds, the AUV's control surfaces become ineffective. To improve an AUV's low speed maneuverability, while maintaining a low drag profile, through-body tunnel thrusters have become a popular addition to modern AUV systems. The effect of forward vehicle motion and sideslip on these types of thrusters is not well understood. In order to characterize these effects and to adapt existing tunnel thruster models to include them, an experimental system was constructed. This system includes a transverse tunnel thruster mounted in a streamlined AUV. A 6-axis load cell mounted internally was used to measure the thrust directly. The AUV was mounted in Memorial University of Newfoundland's tow tank, and several tests were run to characterize the effect of vehicle motion on the transient and steady state thruster performance. Finally, a thruster model was modified to include these effects.

Design of an adaptive controller for dive-plane control of a torpedo-shaped AUV

Underwater vehicles operating in complex ocean conditions present difficulties in determining accurate dynamic models. To guarantee robustness against parameter uncertainty, an adaptive controller for dive-plane control, based on Lyapunov theory and back-stepping techniques, was proposed. In the closed-loop system, asymptotic tracking of the reference depth and pitch angle trajectories was accomplished. Simulation results were presented which show effective dive-plane control in spite of the uncertainties in the system parameters.

Motion Controller for Autonomous Underwater Vehicle Based on Parallel Neural Network

This paper proposes a novel motion controller for autonomous underwater vehicle based on parallel neural network. The motion controller consists of a real-time part, a self-learning part and a desired state programming part, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. Finally, simulation is carried out on ZS-IV vehicle. The control performance is compared with that of neural network controller with different structures such as normal adaptive neural network. The results show that the AUV motion controller based on parallel neural network has a great possibility to solve the problems in the AUV control system design.

AUV modeling and motion control strategy design

To provide a suitable model for AUV simulation and control purposes, a general nonlinear dynamic model including a novel thruster hydrodynamics model was derived. Based on the modeling method, the “AUV-XX” simulation platform was established to carry out fundamental tests on its motion characteristics, stability, and controllability. A motion control strategy consisting of both position and speed control in a horizontal plane was designed for different task assignments of underwater vehicles. Combined control of heave and pitch was adopted to compensate for the reduction of vertical tunnel thrusters when the vehicle is moving at a high speed. An improved S-surface controller based on the capacitor plate model was developed with flexible gain selections made possible by different forms of restricting the error and changing the rate of the error. Simulation results show that the derived general mathematical model together with simulation platform can provide a test bed for fundamental tests of motion control. Additionally, the capacitor plate model S-surface control shows a good performance in guiding the vehicle to achieve the desired position and speed with sufficient accuracy.

A Self-Tuning Proportional-Integral-Derivative Controller for an Autonomous Underwater Vehicle, Based On Taguchi Method

Problem statement: Conventional Proportional-Integral-Derivative (PID) controllers exhibit moderately good performance once the PID gains are properly tuned. However, when the dynamic characteristics of the system are time dependent or the operating conditions of the system vary, it is necessary to retune the gains to obtain desired performance. This situation has renewed the interest of researchers and practitioners in PID control. Self-tuning of PID controllers has emerged as a new and active area of research with the advent and easy availability of algorithms and computers. This study discusses self-tuning (auto-tuning) algorithm for control of autonomous underwater vehicles. Approach: Self-tuning mechanism will avoid time consuming manual tuning of controllers and promises better results by providing optimal PID controller settings as the system dynamics or operating points change. Most of the self-tuning methods available in the literature were based on frequency response characteristics and search methods. In this study, we proposed a method based on Taguchi’s robust design method for self-tuning of an autonomous underwater vehicle controller. The algorithm, based on this method, tuned the controller gains optimally and robustly in real time with less computation effort by using desired and actual state variables. It can be used for the Single-Input Single-Output (SISO) systems as well as Multi-Input Multi-Output (MIMO) systems without mathematical models of plants. Results: A simulation study of the AUV control on the horizontal plane (yaw plane control) was used to demonstrate and validate the performance and effectiveness of the proposed scheme. Simulation results of the proposed self-tuning scheme are compared with the conventional PID controllers which are tuned by Ziegler-Nichols (ZN) and Taguchi’s tuning methods. These results showed that the Integral Square Error (ISE) is significantly reduced from the conventional controllers. The robustness of this proposed self-tuning method was verified and results are presented through numerical simulations using an experimental underwater vehicle model under different working conditions. Conclusion/Recommendations: By using this scheme, the PID controller gains are optimally adjusted automatically online with respect to the system dynamics or operating condition changes. This technique found to be more effective than conventional tuning methods and it is even very convenient when mathematical models of plants are not available. Computer simulations showed that the proposed method has very good tracking performance and robustness even in the presence of disturbances. The simple structure, robustness and ease of computation of the proposed method make it very attractive for real time implementation for controlling of underwater vehicle and it offers a chance to extend the same technique to the three dimensional vehicle tracking control as well.

小型 AUV “MR-X1”のスラスターによる運動制御

小型 AUV “MR-X1”のスラスターによる運動制御

Optimal Yaw Regulation and Trajectory Control of Biorobotic AUV Using Mechanical Fins Based on CFD Parametrization

This paper treats the question of control of a biorobotic autonomous undersea vehicle (BAUV) in the yaw plane using a biomimetic mechanism resembling the pectoral fins of fish. These fins are assumed to undergo a combined sway-yaw motion and the bias angle is treated as a control input, which is varied in time to accomplish the maneuver in the yaw-plane. The forces and moments produced by the flapping foil are parametrized using computational fluid dynamics. A finite-difference-based, Cartesian grid immersed boundary solver is used to simulate flow past the flapping foils. The periodic forces and moments are expanded as a Fourier series and a discrete-time model of the BAUV is developed for the purpose of control. An optimal control system for the set point control of the yaw angle and an inverse control law for the tracking of time-varying yaw angle trajectories are designed. Simulation results show that in the closed-loop system, the yaw angle follows commanded sinusoidal trajectories and the segments of the intersample yaw trajectory remain close to the discrete-time reference trajectory. It is also found that the fins suitably located near the center of mass of the vehicle provide better maneuverability.

S2 BHCA—Multiple AUVs cooperation oriented control architecture

Oceanographic survey, or other similar applications should be the applications of multiple AUVs. In this paper, the skill & simulation based hybrid control architecture (S2BHCA) as the controller's design reference was proposed. It is a multi-robot cooperation oriented intelligent control architecture based on hybrid ideas. The S2BHCA attempts to incorporate the virtues of the reactive controller and of the deliberative controller by introducing the concept of the “skill”. The additional online task simulation ability for cooperation is supported, too. As an application, a multiple AUV control system was developed with three “skills” for the MCM mission including two different cooperative tasks. The simulation and the sea trials show that simple task expression, fast reaction and better cooperation support can be achieved by realizing the AUV controller based on the S2BHCA.

Adaptive AUV Control for Optimized Swathe Laser Stripe Imaging

As part of a three year collaborative research project funded by EPSRC involving both Cranfield Universe and the University of Plymouth, a Deep Mobile Target (DMT5) torpedo-shaped underwater vehicle has be developed into an AUV called Hammerhead. In addition to several low cost navigation sensors, the AUV has be installed with a laser-assisted imaging and navigation sensor. This sensor is based on the laser stripe illumination (LSI) methodology previously developed at Cranfield University (Tetlow and Allwood, 1995), providing enhance viewing of the seabed below the vehicle. However, it also provides real time altitude and seabed-relative translation velocity data to the navigation system during the mission (Dalgleish et al., 2003), as well as gather images to product a post mission enhanced optical waterfall image of a surveyed area (Dalgleish et al., 2004).For a particular water type and clarity, the optimal stand-off distance (altitude and hence swathe) between the LSI a the seabed will vary. The optimisation problem is essentially the pursuit of a balance between image serve efficiency and image quality. This paper describes the development of an automatic process for monitoring the I image quality to determine the optimum altitude at which to fly, and henceforth instruct the AUV control system do so. In this way, swathe may be optimised for maximum seabed coverage and more efficient use of AUV time. 1 paper also presents simulation results from a genetic algorithm (GA) based model predictive control (MI methodology used to track the required altitude provided by this process.

2A1-2F-A7 自律型海中ロボットの潮流中における燃料最適誘導航法

Since there exists more or less sea current in most of underwater environments, ingenious guidance can lead an AUV to reach the terminal position with reduced consumption of fuel. This may be one of the most critical progresses in the navigation of AUV, since it always suffers from the restricted amount of fuel storage. This paper addresses the development of fuel-optimal guidance and tracking control of AUV when it transfers between two pre-designated points in the region sea current. Strategy of fuel-optimal guidance is applied to the navigation of an AUV "R-One Robot", developed by IIS university of Tokyo. In the formulation of this guidance problem, position vector of the vehicle relative to inertial frame is treated as the state vector, while the vehicle heading as the input. Total amount of fuel consumption to reach the terminal position is considered as the performance index to be minimized. Optimal guidance law was formulated as an ordinary differential equation of two-point boundary value problem using the calculus of variations. Application of optimal guidance shows especially apparent savings of fuel consumption, when the local distribution of sea current becomes more complex and irregular.

An Overview on Behaviour-Based Methods for AUV Control

Traditionally, deliberative reasoning methods based on a world model have been used to guide Autonomous Vehicles. Nevertheless, due to serious problems when dealing with complex and changing environments the alternative approach of reactive architectures appeared, directly coupling perception and action without any world representation. This paper describes the characteristics and variants of reactive architectures as well as four of the most suitable ones to guide Autonomous Underwater Vehicles: Schema-based approach, Subsumption, Process Description Language and Action Selection Dynamics. Each architecture is described and evaluated using an underwater simulator with an AUV model.

Adaptive Control of AUV: Fault Accommodation

In this article an indirect adaptive control method based on a hybrid system identification environment consisting of an Artificial Neural Network (ANN) and a parameter estimator is introduced which manages variable speed navigation, unmodeled dynamics and faults. While the ANN delivers a one step ahead prediction of output of a nonlinear function representing the unmodeled dynamics/additive faults, the parameter estimation technique provides an evaluation of the parameters of the unknown system which may also vary due to changes in the operating conditions and or multiplicative faults.

Object-Oriented Control Systems and Real-Time Kernels

We describe some of our experiences from the development of a real-time control system for an autonomous underwater vehicle. This type of control system has to handle non-predictable events originating in a partly unknown world. We call this a reactive real time control system. Object oriented methods seem to be particularly appropriate for reactive realtime systems. We have developed an object oriented framework for this type of system that is called “Administrative System for AUV Control Software” - ASACS. This framework is special in that the objects in the control system represent state variables, e.g., speed and heading, instead of physical entities like motor and rudder. We believe that the state oriented objects are the more suitable for making a generalized framework like ASACS.

Object-oriented software system for AUV control

SINTEF has developed an object-oriented software architecture for advanced control systems called ASACS. We have also developed a corresponding operating system which makes it relatively simple to implement a control system in the ASACS architecture. The main objects in ASACS are “state variables”. Each state variable is a data container that represent one of the axes in the system's state space. The state variables contain information. Knowledge, in the form of functions and procedures, is hidden behind the state variables. The focus on information instead of function has benefits with respect to easy reconfiguration, reuse, and reliability. ASACS is not intended for systems with a high degree of “intelligence”. We aim at developing real-time control systems which have limited flexibility, but a high degree of determinism and reliability. ASACS has been tested on a small control system. The test showed that the architecture and the administrative programs worked as expected. The application system was easy to write and simple to expand.