Applied Model-Based Analysis and Synthesis for the Dynamics, Guidance, and Control of an Autonomous Undersea Vehicle

Model-based analysis and synthesis applied to the dynamics, guidance, and control of an autonomous undersea vehicle are presented. As the dynamic model for describing vehicle motion mathematically, the equations of motion are derived. The stability derivatives in the equations of motion are determined by a simulation-based technique using computational fluid dynamics analysis. The dynamic model is applied to the design of the low-level control systems, offering model-based synthetic approach in dynamics and control applications. As an intelligent navigational strategy for undersea vehicles, we present the optimal guidance in environmental disturbances. The optimal guidance aims at the minimum-time transit of a vehicle in an environmental flow disturbance. In this paper, a newly developed algorithm for obtaining the numerical solution of the optimal guidance law is presented. The algorithm is a globally working procedure deriving the optimal guidance in any deterministic environmental disturbance. As a fail-safe tactic in achieving the optimal navigation in environments of moderate uncertainty, we propose the quasi-optimal guidance. Performances of the optimal and the quasi-optimal guidances are demonstrated by the simulated navigations in a few environmental disturbances

Real Time Underwater Obstacle Avoidance and Path Re-planning Using Simulated Multi-beam Forward Looking Sonar Images for Autonomous Surface Vehicle

This paper describes underwater obstacle avoidance and path re-planning techniques for autonomous surface vehicle (ASV) based on simulated multi-beam forward looking sonar images. The sonar image is first simulated and then a circular obstacle is defined and created in the field of view of the sonar. In this study, the robust real-time path re-planning algorithm based on an A* algorithm is developed. Our real-time path re-planning algorithm has been tested to regenerate the optimal path for several updated frames with a proper update frequency between the start point and the goal point both in static and dynamical environments. The performance of proposed method is verified through simulations, and tank experiments using an actual ASV. While the simulation results are successful, the vehicle model can avoid both single obstacle, multiple obstacles and moving obstacle with the optimal trajectory. For tank experiments, the proposed method for underwater obstacle avoidance system is implemented with the ASV test platform. The vehicle is controlled in real-time and moderately succeeds in its avoidance against the obstacle simulated in the field of view of the sonar together with the proposed position stochastic estimation of the vehicle

Thrust control design for unmanned marine vehicles

Thesis (Master)--Izmir Institute of Technology, Mechanical Engineering, Izmir, 2012Includes bibliographical references (leaves: 65-70)Text in English; Abstract: Turkish and Englishxv, 74 leavesIn conventional electrically driven propulsion systems with fixed pitch propellers, thruster controllers are usually aimed at controlling propeller shaft speed only. Especially in unmanned marine vehicles which operate in dynamic flow conditions, these type thruster controllers provide unsatisfactory thrust responses. The reason for this is that the thrust force is simultaneously affected by dynamic effects like, variable ambient flow velocity and angle, thruster-thruster interaction and ventilation. It is aimed to achieve acceptable thrust tracking accuracy in all kind of dynamic flow conditions in this thesis work. A novel feed-back based thruster controller which includes the effect of incoming axial flow velocity, is designed for this purpose. In controller design, first, thruster propeller's open water characteristics in four-quadrant flow states are measured. Data collected from open water tests are then non-dimensionalized and embedded in the controller's thrust model code. Relation between ideal shaft speed and desired thrust is derived by using the four-quadrant propeller model. The proposed method is evaluated in the experimental test-setup designed for this study to simulate open water conditions. Results indicate that thrust tracking performance of novel controller is acceptable in all four-quadrant flow tests

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development

통합형 무인 수상선-케이블-수중선 시스템의 다물체동역학 거동 및 제어

Underwater exploration is becoming more and more important, since a vast range of unknown resources in the deep ocean remain undeveloped. This dissertation thus presents a modeling of the coupled dynamics of an Unmanned Surface Vehicle (USV) system with an Underwater Vehicles (UV) connected by an underwater cable (UC). The complexity of this multi-body dynamics system and ocean environments is very difficult to model. First, for modeling this, dynamics analysis was performed on each subsystem and further total coupled system dynamics were studied. The UV which is towed by a UC is modeled with 6-DOF equations of motion that reflects its hydrodynamic characteristic was studied. The 4th-order Runge–Kutta numerical method was used to analyze the motion of the USV with its hydrodynamic coefficients which were obtained through experiments and from the literature. To analyze the effect of the UC, the complicated nonlinear and coupled UC dynamics under currents forces, the governing equations of the UC dynamics are established based on the catenary equation method, then it is solved by applying the shooting method. The new formulation and solution of the UC dynamics yields the three dimensional position and forces of the UC end point under the current forces. Also, the advantage of the proposed method is that the catenary equations using shooting method can be solved in real time such that the calculated position and forces of UC according to time can be directly utilized to calculate the UV motion. The proposed method offers advantages of simple formulation, convenient use, and fast calculation time with exact result. Some simple numerical simulations were conducted to observe the dynamic behaviors of AUV with cable effects. The simulations results clearly reveal that the UC can greatly influence the motions of the vehicles, especially on the UV motions. Based on both the numerical model and simulation results developed in the dissertation, we may offer some valuable information for the operation of the UV and USV. Secondly, for the design controller, a PD controller and its application to automatic berthing control of USV are also studied. For this, a nonlinear mathematical model for the maneuvering of USV in the presence of environmental forces was firstly established. Then, in order to control rudder and propeller during automatic berthing process, a PD control algorithm is applied. The algorithm consists of two parts, the forward velocity control and heading angle control. The control algorithm was designed based on the longitudinal and yaw dynamic models of USV. The desired heading angle was obtained by the so-called “Line of Sight” method. To support the validity of the proposed method, the computer simulations of automatic USV berthing are carried out. The results of simulation showed good performance of the developed berthing control system. Also, a hovering-type AUV equipped with multiple thrusters should maintain the specified position and orientation in order to perform given tasks by applying a dynamic positioning (DP) system. Besides, the control allocation algorithm based on a scaling factor is presented for distributing the forces required by the control law onto the available set of actuators in the most effective and energy efficient way. Thus, it is necessary for the robust control algorithm to conduct successfully given missions in spite of a model uncertainty and a disturbance. In this dissertation, the robust DP control algorithm based on a sliding mode theory is also addressed to guarantee the stability and better performance despite the model uncertainty and disturbance of current and cable effects. Finally, a series of simulations are conducted to verify the availability of the generated trajectories and performance of the designed robust controller. Thirdly, for the navigation of UV, a method for designing the path tracking controller using a Rapidly-exploring Random Trees (RRT) algorithm is proposed. The RRT algorithm is firstly used for the generation of collision free waypoints. Next, the unnecessary waypoints are removed by a simple path pruning algorithm generating a piecewise linear path. After that, a path smoothing algorithm utilizing cubic Bezier spiral curves to generate a continuous curvature path that satisfies the minimum radius of curvature constraint of underwater is implemented. The angle between two waypoints is the only information required for the generation of the continuous curvature path. In order to underwater vehicle follow the reference path, the path tracking controller using the global Sliding Mode Control (SMC) approach is designed. To verify the performance of the proposed algorithm, some simulation results are performed. Simulation results showed that the RRT algorithm could be applied to generate an optimal path in a complex ocean environment with multiple obstacles.Acknowledgement .................................................................................................. vi Abstract……. ....................................................................................... ………….viii Nomenclature ....................................................................................................... xvi List of Abbreviations ........................................................................................... xxi List of Tables ...................................................................................................... xxiii List of Figures ..................................................................................................... xxiv Chapter 1: Introduction ......................................................................................... 1 1.1 Background .................................................................................................. 1 1.1.1 Unmanned Surface Vehicles (USVs) ...................................................... 1 1.1.2 Umbilical Cable ....................................................................................... 4 1.1.3 Unmanned Underwater Vehicles (UUVs) ............................................... 5 1.1.4 Literature on Modeling of Marine Vehicles ............................................ 9 1.1.5 Literature on Control and Guidance of Marine Vehicles ...................... 11 1.2 Our System Architecture ........................................................................... 12 1.3 Motivation ................................................................................................. 13 1.4 Contribution ............................................................................................... 16 1.5 Publications Associated to the Dissertation .............................................. 17 1.6 Structure of the Dissertation ...................................................................... 18 Chapter 2: Mathematical Model of Unmanned Surface Vehicle (USV) ......... 20 2.1 Basic Assumptions .................................................................................... 20 2.2 Three Coordinate Systems ......................................................................... 20 2.3 Variable Notation ...................................................................................... 22 2.4 Kinematics ................................................................................................. 23 2.5 Kinetics ...................................................................................................... 26 2.5.1 Rigid Body Equations of Motion ........................................................... 26 2.5.2 Hydrodynamic Forces and Moments ..................................................... 28 2.5.3 Restoring Forces and Moments ............................................................. 31 2.5.4 Environmental Disturbances .................................................................. 32 2.5.5 Propulsion Forces and Moments ........................................................... 35 2.6 Nonlinear 6DOF Dynamics ....................................................................... 35 2.7 Mathematical Model of USV in 3 DOF .................................................... 36 2.7.1 Planar Kinematics .................................................................................. 36 2.7.2 Planar Nonlinear 3 DOF Dynamics ....................................................... 38 2.8 Configuration of Thrusters ........................................................................ 40 2.9 General Structure and Model Parameters .................................................. 41 2.9.1 Structure of USV ................................................................................... 41 2.9.2 Control System of USV ......................................................................... 42 2.9.3 Winch Control System ........................................................................... 43 Chapter 3: Mathematical Model of the Umbilical Cable (UC) ........................ 45 3.1 Basic Assumptions for UC ........................................................................ 45 3.2 Analysis on Forces of UV ......................................................................... 47 3.3 Relation for UC Equilibrium ..................................................................... 50 3.4 Catenary Equation in the Space Case ........................................................ 51 3.5 Shooting Method ....................................................................................... 55 3.6 Boundary Conditions ................................................................................. 57 3.7 Cable Effects ............................................................................................. 58 3.8 Model Parameters and Simulation ............................................................. 59 Chapter 4: Mathematical Model of Underwater Vehicle (UV) ........................ 63 4.1 Background ................................................................................................ 63 4.1.1 Basic Assumptions................................................................................. 63 4.1.2 Reference Frames .................................................................................. 64 4.1.3 Notations ................................................................................................ 65 4.2 Kinematics Equations ................................................................................ 66 4.3 Kinetic Equations ...................................................................................... 67 4.3.1 Rigid-Body Kinetics .............................................................................. 67 4.3.2 Hydrostatic Terms ................................................................................. 69 4.3.3 Hydrodynamic Terms ............................................................................ 70 4.3.4 Actuator Modeling ................................................................................. 75 4.3.5 Umbilical Cable Forces ......................................................................... 75 4.4 Nonlinear Equations of Motion (6DOF) ................................................... 76 4.5 Simplification of UV Dynamic Model ...................................................... 77 4.5.1 Simplifying the Mass and Inertia Matrix ............................................... 78 4.5.2 Simplifying the Hydrodynamic Damping Matrix.................................. 79 4.5.3 Simplifying the Gravitational and Buoyancy Vector ............................ 80 4.6 Thruster Modeling ..................................................................................... 80 4.7 Current Modeling ...................................................................................... 83 4.8 Dynamic Model Including Ocean Currents ............................................... 84 4.9 Complete Motion Equations of AUV (6DOF) .......................................... 89 4.10 Dynamics Model Parameter Identification ................................................ 91 4.11 Numerical Solution for Equations of Motion ............................................ 93 4.12 General Structure and Model Parameters .................................................. 94 4.12.1 Structure of AUV ............................................................................... 94 4.12.2 Control System of AUV ..................................................................... 96 Chapter 5: Guidance Theory ............................................................................... 97 5.1 Configuration of GNC System .................................................................. 97 5.1.1 Guidance ................................................................................................ 98 5.1.2 Navigation .............................................................................................. 98 5.1.3 Control ................................................................................................... 98 5.2 Maneuvering Problem Statement .............................................................. 99 5.3 Guidance Objectives ................................................................................ 100 5.3.1 Target Tracking ................................................................................... 100 5.3.2 Trajectory Tracking ............................................................................. 100 5.4 Waypoint Representation ........................................................................ 101 5.5 Path Following ......................................................................................... 102 5.6 Line of Sight (LOS) Waypoint Guidance ................................................ 102 5.6.1 Enclosure-Based Steering .................................................................... 104 5.6.2 Look-ahead Based Steering ................................................................. 105 5.6.3 LOS Control......................................................................................... 106 5.7 Cubic Polynomial for Path-Following ..................................................... 107 Chapter 6: Control Algorithm Design and Analysis ....................................... 110 6.1 Proportional Integral Differential (PID) Controller ................................ 110 6.1.1 General Theory .................................................................................... 110 6.1.2 Stability of General PID Controller ..................................................... 112 6.1.3 PID Tuning .......................................................................................... 114 6.1.4 Nonlinear PID for Marine Vehicles ..................................................... 116 6.1.5 Nonlinear PD for Marine Vehicles ...................................................... 117 6.1.6 Stability of Designed PD Controller .................................................... 117 6.2 Sliding Mode Controller .......................................................................... 118 6.2.1 Tracking Error and Sliding Surface ..................................................... 119 6.2.2 Chattering Situation ............................................................................. 120 6.2.3 Control Law and Stability .................................................................... 121 6.3 Allocation Control ................................................................................... 124 6.3.1 Linear Quadratic Unconstrained Control Allocation Using Lagrange Multipliers ................................................................................................ 125 6.3.2 Thruster Allocation with a Constrained Linear Model ........................ 127 6.4 Simulation Results and Discussion ......................................................... 131 6.4.1 Berthing (parking) Control of USV ..................................................... 133 6.4.2 Motion Control of UV ......................................................................... 136 Chapter 7: Obstacle Avoidance and Path Planning for Vehicle Using Rapidly-Exploring Random Trees Algorithm.................................................................. 168 7.1 Path Planning and Guidance: Two Interrelated Problems ....................... 168 7.2 RRT Algorithm for Exploration .............................................................. 171 7.2.1 Random Node Selection ...................................................................... 172 7.2.2 Nearest Neighbor Node Selection ....................................................... 173 7.2.3 RRT Exploration with Obstacles ......................................................... 174 7.3 RRT Algorithm for Navigation of AUV ................................................. 176 7.3.1 Basic RRT Algorithm .......................................................................... 176 7.3.2 Biased-Greedy RRT Algorithm ........................................................... 178 7.3.3 Synchronized Biased-Greedy RRT Algorithm .................................... 179 7.4 Path Pruning ............................................................................................ 182 7.4.1 Path Pruning Using LOS ..................................................................... 182 7.4.2 Global Path Pruning ............................................................................. 183 7.5 Summarize the Proposed RRT Algorithm ............................................... 185 7.6 Simulation for Path Following of AUV .................................................. 187 Chapter 8: Simulation of Complete USV-UC-UV Systems ............................ 196 8.1 Simulation Procedure .............................................................................. 196 8.2 Simulation Results and Discussion ......................................................... 201 8.2.1 Dynamic Behaviors of Complete USV (Stable)-Cable- AUV (Turning Motion) ..................................................................................................... 201 8.2.2 Dynamic Behaviors of Complete USV (Forward motion)-Cable- AUV (Turning Motion) ...................................................................................... 207 8.2.3 Applied Controller to Complete USV -Cable- AUV ........................... 215 Chapter 9: Conclusions and Future Works ..................................................... 238 9.1 Modeling of Complete USV-Cable-AUV System .................................. 238 9.2 Motion Control ........................................................................................ 239 9.3 Cable Force and Moment at the Tow Points ........................................... 239 9.4 Path Planning ........................................................................................... 239 9.5 Future Works ........................................................................................... 240Docto

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties

State relativity and speed-allocated line-of-sight course control for path-following of underwater vehicles

Path-following is a primary task for most marine, air or space crafts, especially during autonomous operations. Research on autonomous underwater vehicles (AUV) has received large interests in the last few decades with research incentives emerging from the safe, cost-effective and practical solutions provided by their applications such as search and rescue, inspection and monitoring of pipe-lines ans sub-sea structures. This thesis presents a novel guidance system based on the popular line-of-sight (LOS) guidance law for path-following (PF) of underwater vehicles (UVs) subject to environmental disturbances. Mathematical modeling and dynamics of (UVs) is presented first. This is followed by a comprehensive literature review on guidance-based path-following control of marine vehicles, which includes revised definitions of the track-errors and more detailed illustrations of the general PF problem. A number of advances on relative equations of motion are made, which include an improved understanding of the fluid FLOW frame and expression of its motion states, an analytic method of modeling the signs of forces and moments and the proofs of passivity and boundedness of relative UV systems in 3-D. The revision in the relative equations of motion include the concept of state relativity, which is an improved understanding of relativity of motion states expressed in reference frames and is also useful in incorporating environmental disturbances. In addition, the concept of drift rate is introduced along with a revision on the angles of motion in 3-D. A switching mechanism was developed to overcome a drawback of a LOS guidance law, and the linear and nonlinear stability results of the LOS guidance laws have been provided, where distinctions are made between straight and curved PF cases. The guidance system employs the unique formulation and solution of the speed allocation problem of allocating a desired speed vector into x and y components, and the course control that employs the slip angle for desired heading for disturbance rejection. The guidance system and particularly the general course control problem has been extended to 3-D with the new definition of vertical-slip angle. The overall guidance system employing the revised relative system model, course control and speed allocation has performed well during path-following under strong ocean current and/or wave disturbances and measurement noises in both 2-D and 3-D scenarios. In 2-D and 3-D 4 degrees-of-freedom models (DOF), the common sway-underactuated and fully actuated cases are considered, and in 3-D 5-DOF model, sway and heave underactuated and fully actuated cases are considered. Stability results of the LOS guidance laws include the semi-global exponential stability (SGES) of the switching LOS guidance and enclosure-based LOS guidance for straight and curved paths, and SGES of the loolahead-based LOS guidance laws for curved paths. Feedback sliding mode and PID controllers are applied during PF providing a comparison between them, and simulations are carried out in MatLab

Deep-Sea Model-Aided Navigation Accuracy for Autonomous Underwater Vehicles Using Online Calibrated Dynamic Models

In this work, the accuracy of inertial-based navigation systems for autonomous underwater vehicles (AUVs) in typical mapping and exploration missions up to 5000m depth is examined. The benefit of using an additional AUV motion model in the navigation is surveyed. Underwater navigation requires acoustic positioning sensors. In this work, so-called Ultra-Short-Baseline (USBL) devices were used allowing the AUV to localize itself relative to an opposite device attached to a (surface) vehicle. Despite their easy use, the devices\u27 absolute positioning accuracy decreases proportional to range. This makes underwater navigation a sophisticated estimation task requiring integration of multiple sensors for inertial, orientation, velocity and position measurements. First, error models for the necessary sensors are derived. The emphasis is on the USBL devices due to their key role in navigation - besides a velocity sensor based on the Doppler effect. The USBL model is based on theoretical considerations and conclusions from experimental data. The error models and the navigation algorithms are evaluated on real-world data collected during field experiments in shallow sea. The results of this evaluation are used to parametrize an AUV motion model. Usually, such a model is used only for model-based motion control and planning. In this work, however, besides serving as a simulation reference model, it is used as a tool to improve navigation accuracy by providing virtual measurements to the navigation algorithm (model-aided navigation). The benefit of model-aided navigation is evaluated through Monte Carlo simulation in a deep-sea exploration mission. The final and main contributions of this work are twofold. First, the basic expected navigation accuracy for a typical deep-sea mission with USBL and an ensemble of high-quality navigation sensors is evaluated. Secondly, the same setting is examined using model-aided navigation. The model-aiding is activated after the AUV gets close to sea-bottom. This reflects the case where the motion model is identified online which is only feasible if the velocity sensor is close to the ground (e.g. 100m or closer). The results indicate that, ideally, deep-sea navigation via USBL can be achieved with an accuracy in range of 3-15m w.r.t. the expected root-mean-square error. This also depends on the reference vehicle\u27s position at the surface. In case the actual estimation certainty is already below a certain threshold (ca. <4m), the simulations reveal that the model-aided scheme can improve the navigation accuracy w.r.t. position by 3-12%

Adaptive Control For Autonomous Navigation Of Mobile Robots Considering Time Delay And Uncertainty

Autonomous control of mobile robots has attracted considerable attention of researchers in the areas of robotics and autonomous systems during the past decades. One of the goals in the field of mobile robotics is development of platforms that robustly operate in given, partially unknown, or unpredictable environments and offer desired services to humans. Autonomous mobile robots need to be equipped with effective, robust and/or adaptive, navigation control systems. In spite of enormous reported work on autonomous navigation control systems for mobile robots, achieving the goal above is still an open problem. Robustness and reliability of the controlled system can always be improved. The fundamental issues affecting the stability of the control systems include the undesired nonlinear effects introduced by actuator saturation, time delay in the controlled system, and uncertainty in the model. This research work develops robustly stabilizing control systems by investigating and addressing such nonlinear effects through analytical, simulations, and experiments. The control systems are designed to meet specified transient and steady-state specifications. The systems used for this research are ground (Dr Robot X80SV) and aerial (Parrot AR.Drone 2.0) mobile robots. Firstly, an effective autonomous navigation control system is developed for X80SV using logic control by combining ‘go-to-goal’, ‘avoid-obstacle’, and ‘follow-wall’ controllers. A MATLAB robot simulator is developed to implement this control algorithm and experiments are conducted in a typical office environment. The next stage of the research develops an autonomous position (x, y, and z) and attitude (roll, pitch, and yaw) controllers for a quadrotor, and PD-feedback control is used to achieve stabilization. The quadrotor’s nonlinear dynamics and kinematics are implemented using MATLAB S-function to generate the state output. Secondly, the white-box and black-box approaches are used to obtain a linearized second-order altitude models for the quadrotor, AR.Drone 2.0. Proportional (P), pole placement or proportional plus velocity (PV), linear quadratic regulator (LQR), and model reference adaptive control (MRAC) controllers are designed and validated through simulations using MATLAB/Simulink. Control input saturation and time delay in the controlled systems are also studied. MATLAB graphical user interface (GUI) and Simulink programs are developed to implement the controllers on the drone. Thirdly, the time delay in the drone’s control system is estimated using analytical and experimental methods. In the experimental approach, the transient properties of the experimental altitude responses are compared to those of simulated responses. The analytical approach makes use of the Lambert W function to obtain analytical solutions of scalar first-order delay differential equations (DDEs). A time-delayed P-feedback control system (retarded type) is used in estimating the time delay. Then an improved system performance is obtained by incorporating the estimated time delay in the design of the PV control system (neutral type) and PV-MRAC control system. Furthermore, the stability of a parametric perturbed linear time-invariant (LTI) retarded type system is studied. This is done by analytically calculating the stability radius of the system. Simulation of the control system is conducted to confirm the stability. This robust control design and uncertainty analysis are conducted for first-order and second-order quadrotor models. Lastly, the robustly designed PV and PV-MRAC control systems are used to autonomously track multiple waypoints. Also, the robustness of the PV-MRAC controller is tested against a baseline PV controller using the payload capability of the drone. It is shown that the PV-MRAC offers several benefits over the fixed-gain approach of the PV controller. The adaptive control is found to offer enhanced robustness to the payload fluctuations

Fachzeitschrift für Hydrographie und Geoinformation

Second International Issu