Scientific and Technical Aerospace Reports PDF Download
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Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 556
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Book Description
Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 556
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Book Description
Author: Gerasimos Rigatos
Publisher: Cambridge Scholars Publishing
ISBN: 1527562468
Category : Technology & Engineering
Languages : en
Pages : 741
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Book Description
This volume discusses advances in applied nonlinear optimal control, comprising both theoretical analysis of the developed control methods and case studies about their use in robotics, mechatronics, electric power generation, power electronics, micro-electronics, biological systems, biomedical systems, financial systems and industrial production processes. The advantages of the nonlinear optimal control approaches which are developed here are that, by applying approximate linearization of the controlled systems’ state-space description, one can avoid the elaborated state variables transformations (diffeomorphisms) which are required by global linearization-based control methods. The book also applies the control input directly to the power unit of the controlled systems and not on an equivalent linearized description, thus avoiding the inverse transformations met in global linearization-based control methods and the potential appearance of singularity problems. The method adopted here also retains the known advantages of optimal control, that is, the best trade-off between accurate tracking of reference setpoints and moderate variations of the control inputs. The book’s findings on nonlinear optimal control are a substantial contribution to the areas of nonlinear control and complex dynamical systems, and will find use in several research and engineering disciplines and in practical applications.
Author:
Publisher:
ISBN:
Category : Airplanes
Languages : en
Pages : 628
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Book Description
Author: John T. Betts
Publisher: SIAM
ISBN: 0898716888
Category : Mathematics
Languages : en
Pages : 442
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Book Description
A focused presentation of how sparse optimization methods can be used to solve optimal control and estimation problems.
Author: Lalo Magni
Publisher: Springer Science & Business Media
ISBN: 3642010938
Category : Technology & Engineering
Languages : en
Pages : 562
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Book Description
Over the past few years significant progress has been achieved in the field of nonlinear model predictive control (NMPC), also referred to as receding horizon control or moving horizon control. More than 250 papers have been published in 2006 in ISI Journals. With this book we want to bring together the contributions of a diverse group of internationally well recognized researchers and industrial practitioners, to critically assess the current status of the NMPC field and to discuss future directions and needs. The book consists of selected papers presented at the International Workshop on Assessment an Future Directions of Nonlinear Model Predictive Control that took place from September 5 to 9, 2008, in Pavia, Italy.
Author: Fabian Russell Pfitz
Publisher: Logos Verlag Berlin GmbH
ISBN: 3832557059
Category :
Languages : en
Pages : 160
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Book Description
In this thesis, we present novel model predictive control (MPC) formulations based on a convex open-loop optimal control problem to tackle the problem setup of trajectory tracking and path following as well as the control of systems with unknown system dynamic. In particular, we consider the framework of relaxed barrier function based MPC (rbMPC). We extend the existing stability theory to the trajectory tracking and the path following problem. We establish important system theoretic properties like closed-loop stability and exact constraint satisfaction under suitable assumptions. Moreover, we evaluate the developed MPC algorithms in the area of automated driving in simulations as well as in a real-world driving scenario. Further, we consider the control of completely unknown systems based on online optimization. We divide the overall problem into the design of an estimation algorithm and a control algorithm. The control algorithm is a model-independent receding horizon control algorithm in which important system theoretic properties like convergence to the origin are guaranteed without the knowledge of the true system parameters. The estimation and control algorithm are combined together and convergence to the origin of the closed-loop system for fully unknown linear time-invariant discrete-time systems is shown.
Author: Johan A.K. Suykens
Publisher: Springer Science & Business Media
ISBN: 1475724934
Category : Technology & Engineering
Languages : en
Pages : 242
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Book Description
Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq emTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.
Author: Runqi Chai
Publisher: Springer Nature
ISBN: 9819943116
Category : Technology & Engineering
Languages : en
Pages : 272
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Book Description
This book focuses on the design and application of advanced trajectory optimization and guidance and control (G&C) techniques for aerospace vehicles. Part I of the book focuses on the introduction of constrained aerospace vehicle trajectory optimization problems, with particular emphasis on the design of high-fidelity trajectory optimization methods, heuristic optimization-based strategies, and fast convexification-based algorithms. In Part II, various optimization theory/artificial intelligence (AI)-based methods are constructed and presented, including dynamic programming-based methods, model predictive control-based methods, and deep neural network-based algorithms. Key aspects of the application of these approaches, such as their main advantages and inherent challenges, are detailed and discussed. Some practical implementation considerations are then summarized, together with a number of future research topics. The comprehensive and systematic treatment of practical issues in aerospace trajectory optimization and guidance and control problems is one of the main features of the book, which is particularly suitable for readers interested in learning practical solutions in aerospace trajectory optimization and guidance and control. The book is useful to researchers, engineers, and graduate students in the fields of G&C systems, engineering optimization, applied optimal control theory, etc.
Author: Liang Yan
Publisher: Springer Nature
ISBN: 9811966133
Category : Technology & Engineering
Languages : en
Pages : 7455
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Book Description
This book features the latest theoretical results and techniques in the field of guidance, navigation, and control (GNC) of vehicles and aircrafts. It covers a wide range of topics, including but not limited to, intelligent computing communication and control; new methods of navigation, estimation and tracking; control of multiple moving objects; manned and autonomous unmanned systems; guidance, navigation and control of miniature aircraft; and sensor systems for guidance, navigation and control etc. Presenting recent advances in the form of illustrations, tables, and text, it also provides detailed information of a number of the studies, to offer readers insights for their own research. In addition, the book addresses fundamental concepts and studies in the development of GNC, making it a valuable resource for both beginners and researchers wanting to further their understanding of guidance, navigation, and control.
Author: Orestes Llanes-Santiago
Publisher: Springer Nature
ISBN: 3031263618
Category : Technology & Engineering
Languages : en
Pages : 319
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Book Description
This book presents the main results of the 19th Latin American Congress of Automatic Control held in November 2022 in Havana, Cuba. The Congress showed several main research results obtained by researchers from diverse countries in the last four years. Of the papers sent to Congress, 28 were finally accepted for presentation after a rigorous analysis of scientific novelty and quality. For their presentation in this book, the papers were divided into 5 major sections that appear in the following order: Part 1. Robust and Nonlinear Control The main research topics addressed in this part are related to fault-tolerant control loops, control by sliding modes, and robust tuning of PID controllers. Examples of electrical motors and chemical processes are used to demonstrate the feasibility of using the proposed techniques. Part 2. Fault Diagnosis in Industrial Systems Fault diagnosis in industrial plants is a very important topic in the Industry 4.0 paradigm. In this part, new techniques of fault diagnosis in mechanical systems using Poincaré features; a real case study for predicting the time of the remaining job cycle at a water treatment plant; and a predictive fault diagnosis for isolated photovoltaic systems are presented. A novel methodology for detecting and locating cyber-attacks in water distribution networks using computational intelligence tools is also presented. Part 3. Robotic and Autonomous Systems New control strategies for path following for autonomous tractors and unmanned aquatic vehicles are analyzed in this part. Moreover, the important topic related to the battery health-aware model predictive control planning for autonomous racing vehicles and the use of robots for monitoring and remediation applications are examined. Part 4. Modeling, Identification, and Delayed Systems A model-based methodology for the efficient selection of centrifugal pumps; the use of probabilistic Boolean networks in smart grid models; the utilization of PSO metaheuristic algorithm in the selection of a model structure; and two schemes to control high-order delayed systems are among the main topics examined in this part. Part 5. Low-Cost Systems and Biomedical Applications In this part, some applications of low-cost monitoring and control systems and two automatic systems used for the characterization of creatinine in wastes samples during hemodialysis process and differential acquisition of blood pressure are shown.
