The long term objective of the program is to develop predictive technologies to support virtual design and evaluation of underwater vehicles systems. CFD technologies will be used to protect hydrodynamic models for AUVs and those models will be coupled with control system design and modeling tools to allow vehicle conceptual design to be evaluated within the context of a realistic mission. The objectives of this effort were to compare the forces and moments acting on a maneuvering AUV predicted by computational fluid dynamics (CFD) code with similar data collected aboard an operational AUV. In particular, the multi-block Navier-Stokes flow solver UNCLE (Unsteady Computational of Field Equations) was used in this effort.
Underwater vehicles present some difficult and very particular control system design problems. These are often the result of nonlinear dynamics and uncertain models, as well as the presence of sometimes unforeseeable environmental disturbances that are difficult to measure or estimate. Autonomous Underwater Vehicles: Modeling, Control Design, and Simulation outlines a novel approach to help readers develop models to simulate feedback controllers for motion planning and design. The book combines useful information on both kinematic and dynamic nonlinear feedback control models, providing simulation results and other essential information, giving readers a truly unique and all-encompassing new perspective on design. Includes MATLAB® Simulations to Illustrate Concepts and Enhance Understanding Starting with an introductory overview, the book offers examples of underwater vehicle construction, exploring kinematic fundamentals, problem formulation, and controllability, among other key topics. Particularly valuable to researchers is the book’s detailed coverage of mathematical analysis as it applies to controllability, motion planning, feedback, modeling, and other concepts involved in nonlinear control design. Throughout, the authors reinforce the implicit goal in underwater vehicle design—to stabilize and make the vehicle follow a trajectory precisely. Fundamentally nonlinear in nature, the dynamics of AUVs present a difficult control system design problem which cannot be easily accommodated by traditional linear design methodologies. The results presented here can be extended to obtain advanced control strategies and design schemes not only for autonomous underwater vehicles but also for other similar problems in the area of nonlinear control.
This book provides a comprehensive overview of marine control system design related to underwater robotics applications. In particular, it presents novel optimization-based model predictive control strategies to solve control problems appearing in autonomous underwater vehicle applications. These novel approaches bring unique features, such as constraint handling, prioritization between multiple design objectives, optimal control performance, and robustness against disturbances and uncertainties, into the control system design. They therefore form a more general framework to design marine control systems and can be widely applied. Advanced Model Predictive Control for Autonomous Marine Vehicles balances theoretical rigor – providing thorough analysis and developing provably-correct design conditions – and application perspectives – addressing practical system constraints and implementation issues. Starting with a fixed-point positioning problem for a single vehicle and progressing to the trajectory-tracking and path-following problem of the vehicle, and then to the coordination control of a large-scale multi-robot team, this book addresses the motion control problems, increasing their level of challenge step-by-step. At each step, related subproblems such as path planning, thrust allocation, collision avoidance, and time constraints for real-time implementation are also discussed with solutions. In each chapter of this book, compact and illustrative examples are provided to demonstrate the design and implementation procedures. As a result, this book is useful for both theoretical study and practical engineering design, and the tools provided in the book are readily applicable for real-world implementation.
Fluid Dynamics presents the basic development of equations in coordinate-invariant form and their use in solving problems in laminar and turbulent flows. This book presents a thorough examination of fluid dynamics by combining fundamental principles with systematic mathematical, and computational approaches. Chapters 1 and 2 provide the requisites of theoretical fluid dynamics - free from any coordinate system and covering all basic equations. Chapter 3 examines the Navier-Stokes equations and Euler's equations in steady and non-steady curvilinear coordinates. It also discusses the essential aspects of vorticity and stream functions. Chapter 4 describes inviscid incompressible and compressible flows, supplementing the treatment of the boundary layer theory. Chapter 5 treats the exact solutions of the Navier-Stokes equations, classical and modern boundary layer theory, and incompressible and compressible forms of the Navier-Stokes formulations. It also discusses the hyperbolic system of partial differential equations and introduces the idea of characteristics through solutions of the Burger's and related equations. In Fluid Dynamics, the author also: Describes how to transform the complete Navier-Stokes system of equations to general steady and non-steady coordinates Covers the method of vector and tensor analysis Explores laminar flow theory Provides a systematic approach to turbulence theory, starting with an introduction to the linear stability theory Explains the theory of homogeneous and isotropic turbulence as well as the turbulent boundary layers Lays a solid foundation for the theory of turbulence modeling for incompressible and compressible flows Emphasizes learning the techniques for using the derived results in solving physical flow problems on high-speed computers Discusses various numerical techniques for solving boundary layer and Navier-Stokes equations Demonstrates the existence of characteristics in the solution of hyberbolic system of equations through a series of simple examples Fluid Dynamics enables students and professionals to grasp and assimilate a constructive framework for modern fluid dynamics, providing a set of algorithmic tools to create useful physical and computational results.
The increasing reliance on oceans, rivers and waterways in a spectrum of human activities have demonstrated the large demand for advanced marine technologies that facilitate multifarious in-water services and tasks. The autonomous underwater vehicle (AUV) is a representative marine technology which has been contributing continuously to many ocean-related fields. An elaborate control system is essential to AUVs. However, AUVs present difficult control system design problems due to their nonlinear dynamics, the unpredictable environment and the poor knowledge about the hydrodynamic coupling of the vehicle degrees of freedom. When designing the motion controller, the practical constraints on the AUV system such as limited perceiving, computing and actuating capabilities should also be respected. The model predictive control (MPC) is an advanced control technology that leverages optimization to calculate the control command. Thanks to the optimization nature, MPC can conveniently handle the complex nonlinearity in system dynamics as well as the state and control constraints. MPC takes the receding horizon control paradigm which gains satisfactory robustness against model uncertainties and external disturbances. Therefore, MPC is an ideal candidate for solving the AUV motion control problems. On the other hand, since the optimization is solved by iterative numerical algorithms, the obtained control signal is an implicit function of the system state, which complicates the characterization of the closed-loop properties. Moreover, the nonlinear system dynamics makes the online optimization nonlinear programming (NLP) problems. The high computational complexity may cause an issue on the real-time control for embedded platforms with limited computing resources. In order to push the advanced MPC technology towards real-world AUV applications, this PhD dissertation is concerned with fundamental AUV motion control problems and attempts to address the aforementioned challenges and provide novel solutions. This dissertation proceeds with Chapter 1 by providing state-of-the-art introductions to related research areas. The mathematical model used for the AUV motion control is elaborated in Chapter 2. In Chapter 3, we consider the AUV navigation and control problem in constrained workspace. A unified receding horizon optimization framework consisting of the dynamic path planning and the nonlinear model predictive control (NMPC) tracking control is developed. Although the NMPC tracking controller well accommodates the practical constraints on the AUV system, it presents a brand new design philosophy compared with the existing control systems that are implemented on real AUVs. Since the existing AUV control systems are reliable controllers, AUV practitioners tend not to fully replace them but to improve the control performance by adding features. By considering this, in Chapter 4, we develop the Lyapunov-based model predictive control (LMPC) scheme which builds on the existing AUV control system and invoke online optimization to improve the control performance. Chapter 5 focuses on the path following (PF) problem. Unlike the trajectory tracking control which equally emphasizes the spatial and temporal control objectives, the PF control often prioritizes the path convergence over the speed assignment. To incorporate this objective prioritization into the controller design, a novel multi-objective model predictive control (MOMPC) scheme is developed. While the MPC technique provides several salient features (e.g., optimality, constraints handling, objective prioritization, robustness, etc.), those features come at a price: a computational bottleneck is formed by the heavy burden of solving online optimizations in real time. To explicitly address this issue, in Chapter 6, the computational complexity of the MPC algorithms is particularly emphasized. Two novel strategies which potentially alleviate the computational burden of the MPC-based AUV tracking control are proposed. In Chapter 7, some conclusive remarks are provided and a few avenues for future research are identified.
The development of intelligent transportation systems, especially autonomous underwater vehicles, has become significant in marine engineering, with an aim to enhance energy efficiency management and communication systems. This book covers different aspects of optimization of autonomous underwater vehicles and their propulsion systems via machine learning techniques. It further analyses hydrodynamic characteristics including the study of experimental investigation combined with hydrodynamic characteristics backed by MATLAB® codes and simulation study results. Features: Covers utilization of machine learning techniques with a focus on marine science and ocean engineering. Details effect of the intelligent transportation system (ITS) into the sustainable environment and ecology system. Evaluates performance of particle swarm intelligence-based optimization techniques. Reviews propulsion performance of the remote-controlled vehicles based on machine learning techniques. Includes MATLAB® examples and simulation study results. This book is aimed at graduate students and researchers in marine engineering and technology, computer science, and control system engineering.
Handbook of MARINE CRAFT HYDRODYNAMICS AND MOTION CONTROL The latest tools for analysis and design of advanced GNC systems Handbook of Marine Craft Hydrodynamics and Motion Control is an extensive study of the latest research in hydrodynamics, guidance, navigation, and control systems for marine craft. The text establishes how the implementation of mathematical models and modern control theory can be used for simulation and verification of control systems, decision-support systems, and situational awareness systems. Coverage includes hydrodynamic models for marine craft, models for wind, waves and ocean currents, dynamics and stability of marine craft, advanced guidance principles, sensor fusion, and inertial navigation. This important book includes the latest tools for analysis and design of advanced GNC systems and presents new material on unmanned underwater vehicles, surface craft, and autonomous vehicles. References and examples are included to enable engineers to analyze existing projects before making their own designs, as well as MATLAB scripts for hands-on software development and testing. Highlights of this Second Edition include: Topical case studies and worked examples demonstrating how you can apply modeling and control design techniques to your own designs A Github repository with MATLAB scripts (MSS toolbox) compatible with the latest software releases from Mathworks New content on mathematical modeling, including models for ships and underwater vehicles, hydrostatics, and control forces and moments New methods for guidance and navigation, including line-of-sight (LOS) guidance laws for path following, sensory systems, model-based navigation systems, and inertial navigation systems This fully revised Second Edition includes innovative research in hydrodynamics and GNC systems for marine craft, from ships to autonomous vehicles operating on the surface and under water. Handbook of Marine Craft Hydrodynamics and Motion Control is a must-have for students and engineers working with unmanned systems, field robots, autonomous vehicles, and ships. MSS toolbox: https://github.com/cybergalactic/mss Lecture notes: https://www.fossen.biz/wiley Author’s home page: https://www.fossen.biz
first industrial application of MPC was in 1973. A key motivation was to provide better performance than could be obtained with the widely-used PID controller whilst making it easy to replace the PID controller unit or module with his new algorithm. It was the advent of digital control technology and the use of software control algorithms that made this replacement easier and more acceptable to process engineers. A decade of industrial practice with PFC was reported in the archival literature by Jacques Richalet et al. in 1978 in an important seminal Automatica paper. Around this time, Cutler and Ramaker published the dynamic matrix control algorithm that also used knowledge of future reference signals to determine a sequence of control signal adjustment. Thus, the theoretical and practical development of predictive control methods was underway and subsequent developments included those of generalized predictive control, and the whole armoury of MPC methods. Jacques Richalet’s approach to PFC was to seek an algorithm that was: • easy to understand; • easy to install; • easy to tune and optimise. He sought a new modular control algorithm that could be readily used by the control-technician engineer or the control-instrument engineer. It goes without saying that this objective also forms a good market strategy.
