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Subsea development and production of hydrocarbons is challenging due to remote and harsh conditions. Recent technology development with high speed communication to subsea and downhole equipment has created a new opportunity to both monitor and control abnormal or undesirable events with a proactive and preventative approach rather than a reactive approach. Two specific technology developments are high speed, long-distance fiber optic sensing for production and completion systems and wired pipe for drilling communications. Both of these communication systems offer unprecedented high speed and accurate sensing of equipment and processes that are susceptible to uncontrolled well situations, leaks, issues with flow assurance, structural integrity, and platform stability, as well as other critical monitoring and control issues. The scope of this dissertation is to design monitoring and control systems with new theoretical developments and practical applications. For estimators, a novel `1-norm method is proposed that is less sensitive to data with outliers, noise, and drift in recovering the true value of unmeasured parameters. For controllers, a similar `1-norm strategy is used to design optimal control strategies that utilize a comprehensive design with multivariate control and nonlinear dynamic optimization. A framework for solving large scale dynamic optimization problems with differential and algebraic equations is detailed for estimation and control. A first area of application is in fiber optic sensing and automation for subsea equipment. A post-installable fiber optic clamp is used to transmit structural information for a tension leg platform. A proposed controller automatically performs ballast operations that both stabilize the floating structure and minimize fatigue damage to the tendons that hold the structure in place. A second area of application is with managed pressure drilling with moving horizon estimation and nonlinear model predictive control. The purpose of this application is to maximize rate of drilling penetration, maintain pressure in the borehole, respond to unexpected gas influx, detect cuttings loading and pack-off, and better manage abnormal events with the drilling process through automation. The benefit of high speed data accessibility is quantified as well as the potential benefit from a combined control strategy versus separate controllers.
Nonlinear Estimation and Control of Automotive Drivetrains discusses the control problems involved in automotive drivetrains, particularly in hydraulic Automatic Transmission (AT), Dual Clutch Transmission (DCT) and Automated Manual Transmission (AMT). Challenging estimation and control problems, such as driveline torque estimation and gear shift control, are addressed by applying the latest nonlinear control theories, including constructive nonlinear control (Backstepping, Input-to-State Stable) and Model Predictive Control (MPC). The estimation and control performance is improved while the calibration effort is reduced significantly. The book presents many detailed examples of design processes and thus enables the readers to understand how to successfully combine purely theoretical methodologies with actual applications in vehicles. The book is intended for researchers, PhD students, control engineers and automotive engineers. Hong Chen is a professor at the State Key Laboratory of Automotive Simulation and Control, and the Department of Control Science and Engineering at Jilin University. Bingzhao Gao is an associate professor at the State Key Laboratory of Automotive Simulation and Control at Jilin University.
This book analyses recent advances in non-linear state estimation and application of such estimation schemes to industrial systems control. This book is mainly addressed to graduate students, researchers and engineers working on the problems of estimation and control of non-linear dynamical systems. This book comes to address the increasing interest of the engineering community in control systems that process and integrate information coming from various types of sensors. By providing analysis on non-trivial problems of joint estimation and control for non-linear dynamical systems, according to recently developed filtering methods and non-linear control techniques, this book is a useful reference for researchers and engineers.
Grid-based Nonlinear Estimation and its Applications presents new Bayesian nonlinear estimation techniques developed in the last two decades. Grid-based estimation techniques are based on efficient and precise numerical integration rules to improve performance of the traditional Kalman filtering based estimation for nonlinear and uncertainty dynamic systems. The unscented Kalman filter, Gauss-Hermite quadrature filter, cubature Kalman filter, sparse-grid quadrature filter, and many other numerical grid-based filtering techniques have been introduced and compared in this book. Theoretical analysis and numerical simulations are provided to show the relationships and distinct features of different estimation techniques. To assist the exposition of the filtering concept, preliminary mathematical review is provided. In addition, rather than merely considering the single sensor estimation, multiple sensor estimation, including the centralized and decentralized estimation, is included. Different decentralized estimation strategies, including consensus, diffusion, and covariance intersection, are investigated. Diverse engineering applications, such as uncertainty propagation, target tracking, guidance, navigation, and control, are presented to illustrate the performance of different grid-based estimation techniques.
Nonlinear Estimation: Methods and Applications with Deterministic Sample Points focusses on a comprehensive treatment of deterministic sample point filters (also called Gaussian filters) and their variants for nonlinear estimation problems, for which no closed-form solution is available in general. Gaussian filters are becoming popular with the designers due to their ease of implementation and real time execution even on inexpensive or legacy hardware. The main purpose of the book is to educate the reader about a variety of available nonlinear estimation methods so that the reader can choose the right method for a real life problem, adapt or modify it where necessary and implement it. The book can also serve as a core graduate text for a course on state estimation. The book starts from the basic conceptual solution of a nonlinear estimation problem and provides an in depth coverage of (i) various Gaussian filters such as the unscented Kalman filter, cubature and quadrature based filters, Gauss-Hermite filter and their variants and (ii) Gaussian sum filter, in both discrete and continuous-discrete domain. Further, a brief description of filters for randomly delayed measurement and two case-studies are also included. Features: The book covers all the important Gaussian filters, including filters with randomly delayed measurements. Numerical simulation examples with detailed matlab code are provided for most algorithms so that beginners can verify their understanding. Two real world case studies are included: (i) underwater passive target tracking, (ii) ballistic target tracking. The style of writing is suitable for engineers and scientists. The material of the book is presented with the emphasis on key ideas, underlying assumptions, algorithms, and properties. The book combines rigorous mathematical treatment with matlab code, algorithm listings, flow charts and detailed case studies to deepen understanding.
This book gives readers in-depth know-how on methods of state estimation for nonlinear control systems. It starts with an introduction to dynamic control systems and system states and a brief description of the Kalman filter. In the following chapters, various state estimation techniques for nonlinear systems are discussed, including the extended, unscented and cubature Kalman filters. The cubature Kalman filter and its variants are introduced in particular detail because of their efficiency and their ability to deal with systems with Gaussian and/or non-Gaussian noise. The book also discusses information-filter and square-root-filtering algorithms, useful for state estimation in some real-time control system design problems. A number of case studies are included in the book to illustrate the application of various nonlinear filtering algorithms. Nonlinear Filtering is written for academic and industrial researchers, engineers and research students who are interested in nonlinear control systems analysis and design. The chief features of the book include: dedicated coverage of recently developed nonlinear, Jacobian-free, filtering algorithms; examples illustrating the use of nonlinear filtering algorithms in real-world applications; detailed derivation and complete algorithms for nonlinear filtering methods, which help readers to a fundamental understanding and easier coding of those algorithms; and MATLAB® codes associated with case-study applications, which can be downloaded from the Springer Extra Materials website.