A Comprehensive Study on Modelling and Control of Autonomous Underwater Vehicle

Autonomous underwater vehicles (AUV) have become the de facto vehicle for remote operations involving oceanography, inspection, and monitoring tasks. These vehicles operate in different and often challenging environments; hence, the design and development of the AUV involving hydrodynamics and control systems need to be designed in detail. This book chapter presents a study on the modelling and robust control of a research vehicle in the presence of uncertainties. The vehicle's dynamic behaviour is modelled using a 6-degree-of-freedom approach, considering the effect of ocean currents. The level flight requirements for different speeds are derived, and the resulting model is decomposed into horizontal and vertical subsystems for linear analysis. The simulation results presented focus on the efficacy of linear controllers within three key subsystems: depth, yaw, and speed. Moreover, level-flight outcomes are demonstrated for a speed of 4 knots. The nonlinear control strategies employed in this study encompass conventional and sliding-mode control (SMC) methodologies. To ensure accurate tracking performance, the controller design considers the vehicle's dynamics with various uncertainties such as ocean currents, parameter uncertainty, CG (Center of Gravity) deviation and buoyancy variation. Both conventional and nonlinear SMC controllers' outcomes are showcased with a lawn-mowing manoeuvre scenario. A systematic comparison is drawn between the robustness of SMC against disturbances and parameter fluctuations in contrast to conventional controllers. Importantly, these results underscore the trade-off that accompanies SMC's robustness, as it necessitates a higher level of complexity in terms of controller design, intricate implementation intricacies, and the management of chattering phenomena.Comment: Accepted for publication in Assistive Robotics, CRC Press, Taylor & Francis, USA. This is the preprint version of the book chapte

The MRS UAV System: Pushing the Frontiers of Reproducible Research, Real-world Deployment, and Education with Autonomous Unmanned Aerial Vehicles

We present a multirotor Unmanned Aerial Vehicle control (UAV) and estimation system for supporting replicable research through realistic simulations and real-world experiments. We propose a unique multi-frame localization paradigm for estimating the states of a UAV in various frames of reference using multiple sensors simultaneously. The system enables complex missions in GNSS and GNSS-denied environments, including outdoor-indoor transitions and the execution of redundant estimators for backing up unreliable localization sources. Two feedback control designs are presented: one for precise and aggressive maneuvers, and the other for stable and smooth flight with a noisy state estimate. The proposed control and estimation pipeline are constructed without using the Euler/Tait-Bryan angle representation of orientation in 3D. Instead, we rely on rotation matrices and a novel heading-based convention to represent the one free rotational degree-of-freedom in 3D of a standard multirotor helicopter. We provide an actively maintained and well-documented open-source implementation, including realistic simulation of UAV, sensors, and localization systems. The proposed system is the product of years of applied research on multi-robot systems, aerial swarms, aerial manipulation, motion planning, and remote sensing. All our results have been supported by real-world system deployment that shaped the system into the form presented here. In addition, the system was utilized during the participation of our team from the CTU in Prague in the prestigious MBZIRC 2017 and 2020 robotics competitions, and also in the DARPA SubT challenge. Each time, our team was able to secure top places among the best competitors from all over the world. On each occasion, the challenges has motivated the team to improve the system and to gain a great amount of high-quality experience within tight deadlines.Comment: 28 pages, 20 figures, submitted to Journal of Intelligent & Robotic Systems (JINT), for the provided open-source software see http://github.com/ctu-mr

Hardware in the Loop Simulation and Control Design for Autonomous Free Running Ship Models

This paper presents an hardware-in-the-loop (HIL) simulation system tool to test and validate an autonomous free running model system for ship hydrodynamic studies with a view to verification of the code, the control logic and system peripherals. The computer simulation of the plant model in real-time computer does not require the actual physical system and reduces the development cost and time for control design and testing purposes. The HIL system includes: the actual programmable embedded controller along with peripherals and a plant model virtually simulated in a real-time computer. With regard to ship controller design for ship model testing, this study describes a plant model for surge and a Nomoto first order steering dynamics, both implemented using Simulink software suit. The surge model captures a quasi-steady state relationship between surge speed and the propeller rpms, obtained from simple forward speed towing tank tests or derived analytically. The Nomoto first order steering dynamics is obtained by performing the standard turning circle test at model scale. The control logic obtained is embedded in a NI-cRIO based controller. The surge and steering dynamics models are used to design a proportional-derivative controller and an LQR controller. The controller runs a Linux based real-time operating system programmed using LabVIEW software. The HIL simulation tool allows for the emulation of standard ship hydrodynamic tests consisting of straight line, turning circle and zigzag to validate the combined system performance, prior to actual for use in the autonomous free-running tests

Design and Development of an Integrated Mobile Robot System for Use in Simple Formations

In recent years, formation control of autonomous unmanned vehicles has become an active area of research with its many broad applications in areas such as transportation and surveillance. The work presented in this thesis involves the design and implementation of small unmanned ground vehicles to be used in leader-follower formations. This mechatronics project involves breadth in areas of mechanical, electrical, and computer engineering design. A vehicle with a unicycle-type drive mechanism is designed in 3D CAD software and manufactured using 3D printing capabilities. The vehicle is then modeled using the unicycle kinematic equations of motion and simulated in MATLAB/Simulink. Simple motion tasks are then performed onboard the vehicle utilizing the vehicle model via software, and leader-follower formations are implemented with multiple vehicles

Synthesis and Hardware Implementation of an Unmanned Aerial Vehicle Automatic Landing System Utilizing Quantitative Feedback Theory

Approach and landing are among the most difficult flight regimes for automatic control of fixed-wing aircraft. Additional challenges are introduced when working with unmanned aerial vehicles, such as modelling uncertainty and limited gust tolerance. This thesis develops linear discrete-time automatic landing controllers using Quantitative Feedback Theory to ensure control robustness and adequate disturbance rejection. Controllers are developed in simulation and evaluated in flight tests of the low cost Easy Star remote-controlled platform. System identification of the larger Pegasus unmanned aerial vehicle is performed to identify dynamic models from flight data. A full set of controllers are subsequently developed and evaluated in simulation for the Pegasus. The extensive simulation and experimental testing with the Easy Star will reduce the time required to implement the Pegasus control laws, and will reduce the associated risk by validating the core experimental software. It is concluded that the control synthesis process using Quantitative Feedback Theory provides robust controllers with generally adequate performance, based on simulation and hardware results. The Quantitative Feedback Theory framework provides a good method for synthesizing the inner-loop controllers and satisfying performance requirements, but in many of the cases considered here it is found to be impractical for the outer loop designs. The primary recommendations of this work are: perform additional verification flights on the Easy Star; repeat Pegasus system identification for a landing configuration before flight testing the control laws; design and implement a rudder control loop on the Pegasus for control of the vehicle after touchdown

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES

The work in this thesis is concerned with the development of a novel and practical collision avoidance system for autonomous underwater vehicles (AUVs). Synergistically, advanced stochastic motion planning methods, dynamics quantisation approaches, multivariable tracking controller designs, sonar data processing and workspace representation, are combined to enhance significantly the survivability of modern AUVs. The recent proliferation of autonomous AUV deployments for various missions such as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial increase in vehicle autonomy. One matching requirement of such missions is to allow all the AUV to navigate safely in a dynamic and unstructured environment. Therefore, it is vital that a robust and effective collision avoidance system should be forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously increasing its autonomy. This thesis not only provides a holistic framework but also an arsenal of computational techniques in the design of a collision avoidance system for AUVs. The design of an obstacle avoidance system is first addressed. The core paradigm is the application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly developed version for use as a motion planning tool. Later, this technique is merged with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages of the RRT. A novel multi-node version which can also address time varying final state is suggested. Clearly, the reference trajectory generated by the aforementioned embedded planner must be tracked. Hence, the feasibility of employing the linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent Ricatti equation (SDRE) controller as trajectory trackers are explored. The obstacle detection module, which comprises of sonar processing and workspace representation submodules, is developed and tested on actual sonar data acquired in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing techniques applied are fundamentally derived from the image processing perspective. Likewise, a novel occupancy grid using nonlinear function is proposed for the workspace representation of the AUV. Results are presented that demonstrate the ability of an AUV to navigate a complex environment. To the author's knowledge, it is the first time the above newly developed methodologies have been applied to an A UV collision avoidance system, and, therefore, it is considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT

Estimation of Parameters and Design of a Path Following Controller for a Prototype AUV

In order to improve the performance of autonomous underwater vehicles (AUVs) deployed in different applications such as oceanographic survey, search and detection tasks in a given area necessitates the development of an appropriate path following controller which offers a precise and rapid control of the AUVs’ control surfaces and propeller system. In order to design such a vehicle control system, there is a need for good approximation of the vehicles static and dynamic model. Based on a combination of theoretical and empirical data, it can provide a good starting point for vehicle control system development as well as an alternative to the typical trial-and-error methods used for controller design and tuning. As there are no standard procedure for AUV modeling, the simulation of each autonomous underwater vehicle (AUV) represents a new challenge. This thesis describes the development of a six degree of freedom, non-linear simulation model for the prototype AUV. In this model, all the forces which strongly affect the dynamic performance of an AUV such as the external forces and moments resulting from hydrostatics, hydrodynamics, lift and drag, added mass, and the control inputs of the AUV propeller and fins are all defined in terms of vehicle coefficients. Computational Fluid Dynamics along with empirical formulas have been applied to determine the hydrodynamic coefficients of the AUV. In order to model the behavior of the AUV as closely to the real-world system as possible, the equations used for determining the coefficients, as well as those describing the AUVs’ motions were left in non-linear form. Simulation of the AUV motion was achieved using numerical integration techniques of the equations of motion based on the derived coefficients. From the simulation, of the AUV model, results observed led to the development of a controller for the prototype AUV. Sliding Mode Controller was chosen as the desired controller because of its definitive advantages over the PID controller, some of which are the straightforward firmware implementation, use of discrete decision rules which allows the controller to function in hybrid feedback configuration and the fact that it does not suffer from issues related with the drift in controller signal output with time, i.e. latency issues for real time applications. The developed model of the prototype AUV was decoupled into two separate parts namely Heading control and Depth control. State Space Model for each part was derived and a Sliding Mode controller was developed based on the required dynamics of each part. Simulations of the AUV model integrated with Sliding Mode Controller (SMC) was carried out to determine whether the controller was able to direct the motion of the prototype AUV along the desired path, i.e. the level of accuracy of the prototype AUV in path following task

Model-Based Control Using Model and Mechanization Fusion Techniques for Image-Aided Navigation

Unmanned aerial vehicles are no longer used for just reconnaissance. Current requirements call for smaller autonomous vehicles that replace the human in high-risk activities. Many times these activities are performed in GPS-degraded environments. Without GPS providing today\u27s most accurate navigation solution, autonomous navigation in tight areas is more difficult. Today, image-aided navigation is used and other methods are explored to more accurately navigate in such areas (e.g., indoors). This thesis explores the use of inertial measurements and navigation solution updates using cameras with a model-based Linear Quadratic Gaussian controller. To demonstrate the methods behind this research, the controller will provide inputs to a micro-sized helicopter that allows the vehicle to maintain hover. A new method for obtaining a more accurate navigation solution was devised, originating from the following basic setup. To begin, a nonlinear system model was identified for a micro-sized, commercial, off-the-shelf helicopter. This model was verified, then linearized about the hover condition to construct a Linear Quadratic Regulator (LQR). The state error estimates, provided by an Unscented Kalman Filter using simulated image measurement updates, are used to update the navigation solution provided by inertial measurement sensors using strapdown mechanization equations. The navigation solution is used with a reference signal to determine the position and heading error. This error, along with other states, is fed to the LQR, which controls the helicopter. Research revealed that by combining the navigation solution from the INS mechanization block with a model-based navigation solution, and combining the INS error model and system model during the time propagation in the UKF, the navigation solution error decreases by 20%. The equations used for this modification stem from state and covariance combination methods utilized in the Federated Kalman Filter