Robust Controller Design for an Autonomous Underwater Vehicle

Worldwide there has been a surge of interest in Autonomous Underwater Vehicles (AUV). The ability to operate without human intervention is what makes this technology so appealing. On the other hand, the absence of the human narrows the AUV operation to its control system, computing, and sensing capabilities. Therefore, devising a robust control is mandatory to allow the feasibility of the AUV. Motivated by this fact, this thesis aims to present, discuss and evaluate two linear control solutions being proposed for an AUV developed by a consortium led by CEiiA. To allow the controller design, the dynamic model of this vehicle and respective considerations are firstly addressed. Since the purpose is to enable the vehicle’s operation, devising suitable guidance laws becomes essential. A simple waypoint following and station keeping algorithm, and a path following algorithms are presented. To devise the controllers, a linear version of the dynamic model is derived considering a single operational point. Then, through the decoupling of the linear system into three lightly interactive subsystems, four Proportional Integral Derivative controllers (PIDs) are devised for each Degree Of Freedom (DOF) of the vehicle. A Linear Quadratic Regulator (LQR) design, based on the decoupling of the linear model into longitudinal and lateral subsystems is also devised. To allocate the controller output throughout the actuators, a control allocation law is devised, which improves maneuverability of the vehicle. The results present a solid performance for both control methods, however, in this work, LQR proved to be slightly faster than PID.É visível, a nível mundial, um aumento considerável do interesse em Veículos Autónomos Subaquáticos (Autonomous Underwater Vehicles - AUV). O que torna esta tecnologia tão atraente é a capacidade de operar sem intervenção humana. Contudo, a ausência do ser humano restringe a operação do AUV ao seu sistema de controlo, computação e capacidades de detecção. Desta forma, conceber um controlo robusto é obrigatório para viabilizar o AUV. Motivado por este facto, esta tese tem como objetivo apresentar, discutir e avaliar duas soluções de controlo linear, a propor a um AUV desenvolvido por um consórcio liderado pelo CEiiA. Para que o projeto do controlador seja possível, o modelo dinâmico deste veículo e respectivas considerações são primeiramente abordados. Com a finalidade de possibilitar a operação do veículo, torna-se essencial a elaboração de leis de guidance adequadas. Para este efeito são apresentados algorítmos de Waypoint following e Station keeping, e de path following. Para a projeção dos controladores é derivada uma versão linear do modelo dinâmico, considerando um único ponto operacional. Através da separação do modelo linear em três subsistemas são criados quatro controladores Proporcional Integral Derivativo (PID) para cada grau de liberdade (Degree Of Freedom - DOF) do veículo. É também projetado um Regulador Linear Quadrático (LQR), baseado na separação do modelo linear em dois subsistemas, longitudinal e lateral. É ainda apresentada uma lei de alocação de controlo para distribuir o sinal de saída dos controladores pelos diferentes atuadores. Esta provou melhorar a manobrabilidade do veículo. Os resultados finais apresentam um desempenho sólido para ambos os métodos de controlo. No entanto, neste trabalho, o LQR provou ser mais rápido do que o PID

Autonomous homing and docking tasks for an underwater vehicle

This paper briefly introduces a strategy for autonomous homing and docking tasks using an autonomous underwater vehicle. The control and guidance based path following for those tasks are described in this work. A standard sliding mode for controller design is briefly given. The method provides robust motion control efforts for an underwater vehicle’s decoupled system whilst minimising chattering effects. In a guidance system, the vector field based on a conventional artificial potential field method gives a desired trajectory with a use of existing information from sensors in the network. A well structured Line-of-Sight method is used for an AUV to follow the path. It provides guidance for an AUV to follow the predefined trajectory to a required position with the final desired orientation at the dock. Integration of a control and guidance system provides a complete system for this application. Simulation studies are illustrated in the paper

Depth control of autonomous underwater vehicle using discrete time sliding mode controller

This study presents a Discrete Time Sliding Mode Controller (DSMC) application on depth plane of Autonomous Underwater Vehicle (AUV). The main contribution on this work is an implementation of DSMC on NSP AUV II. Sliding Mode Control (SMC) is a robust type of controller and certainly suitable for controlling AUV in the presence of environmental disturbances and uncertainties. DSMC preserves the properties of standard SMC. Linearized dynamic model of NSP AUV II is used in the numerical simulations. Discrete Proportional Integral Derivative (PID) controllers are used for performance comparative analysis. The design of discrete PID and DSMC for NSP AUV II depth is described. Comparative study between the control laws is presented. The simulated results illustrate strong robustness, improve performance and satisfactory stability of DSMC as compared to discrete-time PID controller

The predictive functional control and the management of constraints in GUANAY II autonomous underwater vehicle actuators

Autonomous underwater vehicle control has been a topic of research in the last decades. The challenges addressed vary depending on each research group's interests. In this paper, we focus on the predictive functional control (PFC), which is a control strategy that is easy to understand, install, tune, and optimize. PFC is being developed and applied in industrial applications, such as distillation, reactors, and furnaces. This paper presents the rst application of the PFC in autonomous underwater vehicles, as well as the simulation results of PFC, fuzzy, and gain scheduling controllers. Through simulations and navigation tests at sea, which successfully validate the performance of PFC strategy in motion control of autonomous underwater vehicles, PFC performance is compared with other control techniques such as fuzzy and gain scheduling control. The experimental tests presented here offer effective results concerning control objectives in high and intermediate levels of control. In high-level point, stabilization and path following scenarios are proven. In the intermediate levels, the results show that position and speed behaviors are improved using the PFC controller, which offers the smoothest behavior. The simulation depicting predictive functional control was the most effective regarding constraints management and control rate change in the Guanay II underwater vehicle actuator. The industry has not embraced the development of control theories for industrial systems because of the high investment in experts required to implement each technique successfully. However, this paper on the functional predictive control strategy evidences its easy implementation in several applications, making it a viable option for the industry given the short time needed to learn, implement, and operate, decreasing impact on the business and increasing immediacy.Peer ReviewedPostprint (author's final draft

Design and Control of a Flight-Style AUV with Hovering Capability

The small flight-style Delphin AUV is designed to evaluate the performance of a long range survey AUV with the additional capability to hover and manoeuvre at slow speed. Delphin’s hull form is based on a scaled version of Autosub6000, and in addition to the main thruster and control surfaces at the rear of the vehicle, Delphin is equipped with four rim driven tunnel thrusters. In order to reduce the development cycle time, Delphin was designed to use commercial-off-the-shelf (COTS) sensors and thrusters interfaced to a standard PC motherboard running the control software within the MS Windows environment. To further simplify the development, the autonomy system uses the State-Flow Toolbox within the Matlab/Simulink environment. While the autonomy software is running, image processing routines are used for obstacle avoidance and target tracking, within the commercial Scorpion Vision software. This runs as a parallel thread and passes results to Matlab via the TCP/IP communication protocol. The COTS based development approach has proved effective. However, a powerful PC is required to effectively run Matlab and Simulink, and, due to the nature of the Windows environment, it is impossible to run the control in hard real-time. The autonomy system will be recoded to run under the Matlab Windows Real-Time Windows Target in the near future. Experimental results are used to demonstrating the performance and current capabilities of the vehicle are presented

An Autonomous Surface Vehicle for Long Term Operations

Environmental monitoring of marine environments presents several challenges: the harshness of the environment, the often remote location, and most importantly, the vast area it covers. Manual operations are time consuming, often dangerous, and labor intensive. Operations from oceanographic vessels are costly and limited to open seas and generally deeper bodies of water. In addition, with lake, river, and ocean shoreline being a finite resource, waterfront property presents an ever increasing valued commodity, requiring exploration and continued monitoring of remote waterways. In order to efficiently explore and monitor currently known marine environments as well as reach and explore remote areas of interest, we present a design of an autonomous surface vehicle (ASV) with the power to cover large areas, the payload capacity to carry sufficient power and sensor equipment, and enough fuel to remain on task for extended periods. An analysis of the design and a discussion on lessons learned during deployments is presented in this paper.Comment: In proceedings of MTS/IEEE OCEANS, 2018, Charlesto

Guidance and Robust Control of a Double-Hull Autonomous Underwater Vehicle

The aim of this paper is to present, discuss and evaluate two linear control solutions for an Autonomous Underwater Vehicle (AUV). As guidance solution, a waypoint following and station-keeping algorithm is presented. Then a PID design is proposed, through the decoupling of the linear system into three lightly interactive subsystems. A Linear Quadratic Regulator (LQR) design is also presented, based on the division of the linear system into longitudinal and lateral subsystems. A control allocation law is also presented to deal with the underactuation problems. Both controllers proved robust for this operating point although, regarding performance, and, for the performed simulation, the LQR controller proved more responsive.info:eu-repo/semantics/publishedVersio

A Robust Model Predictive Control Approach for Autonomous Underwater Vehicles Operating in a Constrained workspace

This paper presents a novel Nonlinear Model Predictive Control (NMPC) scheme for underwater robotic vehicles operating in a constrained workspace including static obstacles. The purpose of the controller is to guide the vehicle towards specific way points. Various limitations such as: obstacles, workspace boundary, thruster saturation and predefined desired upper bound of the vehicle velocity are captured as state and input constraints and are guaranteed during the control design. The proposed scheme incorporates the full dynamics of the vehicle in which the ocean currents are also involved. Hence, the control inputs calculated by the proposed scheme are formulated in a way that the vehicle will exploit the ocean currents, when these are in favor of the way-point tracking mission which results in reduced energy consumption by the thrusters. The performance of the proposed control strategy is experimentally verified using a $4$ Degrees of Freedom (DoF) underwater robotic vehicle inside a constrained test tank with obstacles.Comment: IEEE International Conference on Robotics and Automation (ICRA-2018), Accepte