Trajectory Tracking Path Following And Learning In Model Predictive Control
DOWNLOAD eBooks
Download Trajectory Tracking Path Following And Learning In Model Predictive Control PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Trajectory Tracking Path Following And Learning In Model Predictive Control book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Trajectory Tracking Path Following And Learning In Model Predictive Control
DOWNLOAD eBooks
Author : Fabian Russell Pfitz
language : en
Publisher: Logos Verlag Berlin GmbH
Release Date : 2023-08-21
Trajectory Tracking Path Following And Learning In Model Predictive Control written by Fabian Russell Pfitz and has been published by Logos Verlag Berlin GmbH this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-21 with categories.
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.
Nonlinear Model Predictive Control
DOWNLOAD eBooks
Author : Frank Allgöwer
language : en
Publisher: Birkhäuser
Release Date : 2012-12-06
Nonlinear Model Predictive Control written by Frank Allgöwer and has been published by Birkhäuser this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
During the past decade model predictive control (MPC), also referred to as receding horizon control or moving horizon control, has become the preferred control strategy for quite a number of industrial processes. There have been many significant advances in this area over the past years, one of the most important ones being its extension to nonlinear systems. This book gives an up-to-date assessment of the current state of the art in the new field of nonlinear model predictive control (NMPC). The main topic areas that appear to be of central importance for NMPC are covered, namely receding horizon control theory, modeling for NMPC, computational aspects of on-line optimization and application issues. The book consists of selected papers presented at the International Symposium on Nonlinear Model Predictive Control – Assessment and Future Directions, which took place from June 3 to 5, 1998, in Ascona, Switzerland. The book is geared towards researchers and practitioners in the area of control engineering and control theory. It is also suited for postgraduate students as the book contains several overview articles that give a tutorial introduction into the various aspects of nonlinear model predictive control, including systems theory, computations, modeling and applications.
Handbook Of Model Predictive Control
DOWNLOAD eBooks
Author : Saša V. Raković
language : en
Publisher: Springer
Release Date : 2018-09-01
Handbook Of Model Predictive Control written by Saša V. Raković and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-01 with Science categories.
Recent developments in model-predictive control promise remarkable opportunities for designing multi-input, multi-output control systems and improving the control of single-input, single-output systems. This volume provides a definitive survey of the latest model-predictive control methods available to engineers and scientists today. The initial set of chapters present various methods for managing uncertainty in systems, including stochastic model-predictive control. With the advent of affordable and fast computation, control engineers now need to think about using “computationally intensive controls,” so the second part of this book addresses the solution of optimization problems in “real” time for model-predictive control. The theory and applications of control theory often influence each other, so the last section of Handbook of Model Predictive Control rounds out the book with representative applications to automobiles, healthcare, robotics, and finance. The chapters in this volume will be useful to working engineers, scientists, and mathematicians, as well as students and faculty interested in the progression of control theory. Future developments in MPC will no doubt build from concepts demonstrated in this book and anyone with an interest in MPC will find fruitful information and suggestions for additional reading.
Reinforcement Learning For Optimal Feedback Control
DOWNLOAD eBooks
Author : Rushikesh Kamalapurkar
language : en
Publisher: Springer
Release Date : 2018-05-10
Reinforcement Learning For Optimal Feedback Control written by Rushikesh Kamalapurkar and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-10 with Technology & Engineering categories.
Reinforcement Learning for Optimal Feedback Control develops model-based and data-driven reinforcement learning methods for solving optimal control problems in nonlinear deterministic dynamical systems. In order to achieve learning under uncertainty, data-driven methods for identifying system models in real-time are also developed. The book illustrates the advantages gained from the use of a model and the use of previous experience in the form of recorded data through simulations and experiments. The book’s focus on deterministic systems allows for an in-depth Lyapunov-based analysis of the performance of the methods described during the learning phase and during execution. To yield an approximate optimal controller, the authors focus on theories and methods that fall under the umbrella of actor–critic methods for machine learning. They concentrate on establishing stability during the learning phase and the execution phase, and adaptive model-based and data-driven reinforcement learning, to assist readers in the learning process, which typically relies on instantaneous input-output measurements. This monograph provides academic researchers with backgrounds in diverse disciplines from aerospace engineering to computer science, who are interested in optimal reinforcement learning functional analysis and functional approximation theory, with a good introduction to the use of model-based methods. The thorough treatment of an advanced treatment to control will also interest practitioners working in the chemical-process and power-supply industry.
Field And Service Robotics
DOWNLOAD eBooks
Author : Alonzo Kelly
language : en
Publisher: Springer
Release Date : 2010-07-15
Field And Service Robotics written by Alonzo Kelly and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-15 with Technology & Engineering categories.
Robotics is undergoing a major transformation in scope and dimension. From a largely dominant industrial focus, robotics is rapidly expanding into human en- ronments and vigorously engaged in its new challenges. Interacting with, assi- ing, serving, and exploring with humans, the emerging robots will increasingly touch people and their lives. Beyond its impact on physical robots, the body of knowledge robotics has p- duced is revealing a much wider range of applications reaching across diverse research areas and scientific disciplines, such as: biomechanics, haptics, neuros- ences, virtual simulation, animation, surgery, and sensor networks among others. In return, the challenges of the new emerging areas are proving an abundant source of stimulation and insights for the field of robotics. It is indeed at the int- section of disciplines that the most striking advances happen. The Springer Tracts in Advanced Robotics (STAR) is devoted to bringing to the research community the latest advances in the robotics field on the basis of their significance and quality. Through a wide and timely dissemination of critical - search developments in robotics, our objective with this series is to promote more exchanges and collaborations among the researchers in the community and c- tribute to further advancements in this rapidly growing field.
Model Predictive Control System Design And Implementation Using Matlab
DOWNLOAD eBooks
Author : Liuping Wang
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-02-14
Model Predictive Control System Design And Implementation Using Matlab written by Liuping Wang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-02-14 with Technology & Engineering categories.
Model Predictive Control System Design and Implementation Using MATLAB® proposes methods for design and implementation of MPC systems using basis functions that confer the following advantages: - continuous- and discrete-time MPC problems solved in similar design frameworks; - a parsimonious parametric representation of the control trajectory gives rise to computationally efficient algorithms and better on-line performance; and - a more general discrete-time representation of MPC design that becomes identical to the traditional approach for an appropriate choice of parameters. After the theoretical presentation, coverage is given to three industrial applications. The subject of quadratic programming, often associated with the core optimization algorithms of MPC is also introduced and explained. The technical contents of this book is mainly based on advances in MPC using state-space models and basis functions. This volume includes numerous analytical examples and problems and MATLAB® programs and exercises.
Cognitive Systems And Information Processing
DOWNLOAD eBooks
Author : Fuchun Sun
language : en
Publisher: Springer Nature
Release Date : 2023-12-06
Cognitive Systems And Information Processing written by Fuchun Sun and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-06 with Computers categories.
The two-volume set CCIS 1918 and 1919 constitutes the refereed post-conference proceedings of the 8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023, held in Luoyang, China, during August 10–12, 2023. The 52 full papers presented in these proceedings were carefully reviewed and selected from 136 submissions. The papers are organized in the following topical sections: Volume I : Award; Algorithm & Control; and Application. Volume II: Robotics & Bioinformatics; and Vision.
Advances In Mechanism Machine Science And Engineering In China
DOWNLOAD eBooks
Author : Xinjun Liu
language : en
Publisher: Springer Nature
Release Date : 2023-05-31
Advances In Mechanism Machine Science And Engineering In China written by Xinjun Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-31 with Technology & Engineering categories.
This book presents the conference proceedings of the 23rd IFToMM China International Conference on Mechanism and Machine Science & Engineering (IFToMM CCMMS 2022). CCMMS was initiated in 1982, and it is the most important forum held in China for the exchange of research ideas, presentation of technical and scientific achievements, and discussion of future directions in the field of mechanism and machine science. The topics include parallel/hybrid mechanism synthesis and analysis, theoretical & computational kinematics, compliant mechanisms and micro/nano-mechanisms, reconfigurable and metamorphic mechanisms, space structures, mechanisms and materials, structure adaptation in space environment and ground testing, large-scale membrane deployable structures, construction and application of super-scale space systems, cams, gears and combining mechanisms, fluid power mechatronics drivetrain, mechanical design theory and methods, dynamics and vibration control, mechatronics, biologically inspired mechanisms and robotics, medical & rehabilitation robotics, mobile robotics, soft robotics, heavy non-road mobile machine, robot applications, engineering education on mechanisms, machines, and robotics. This book provides a state-of-the-art overview of current advances in mechanism and machine science in China. The inspiring ideas presented in the papers enlighten academic research and industrial application. The potential readers include academic researchers and industrial professionals in mechanism and machine science.
Learning Based Model Predictive Control With Closed Loop Guarantees
DOWNLOAD eBooks
Author : Raffaele Soloperto
language : en
Publisher: Logos Verlag Berlin GmbH
Release Date : 2023-11-13
Learning Based Model Predictive Control With Closed Loop Guarantees written by Raffaele Soloperto and has been published by Logos Verlag Berlin GmbH this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-13 with categories.
The performance of model predictive control (MPC) largely depends on the accuracy of the prediction model and of the constraints the system is subject to. However, obtaining an accurate knowledge of these elements might be expensive in terms of money and resources, if at all possible. In this thesis, we develop novel learning-based MPC frameworks that actively incentivize learning of the underlying system dynamics and of the constraints, while ensuring recursive feasibility, constraint satisfaction, and performance bounds for the closed-loop. In the first part, we focus on the case of inaccurate models, and analyze learning-based MPC schemes that include, in addition to the primary cost, a learning cost that aims at generating informative data by inducing excitation in the system. In particular, we first propose a nonlinear MPC framework that ensures desired performance bounds for the resulting closed-loop, and then we focus on linear systems subject to uncertain parameters and noisy output measurements. In order to ensure that the desired learning phase occurs in closed-loop operations, we then propose an MPC framework that is able to guarantee closed-loop learning of the controlled system. In the last part of the thesis, we investigate the scenario where the system is known but evolves in a partially unknown environment. In such a setup, we focus on a learning-based MPC scheme that incentivizes safe exploration if and only if this might yield to a performance improvement.
Proceedings Of China Sae Congress 2023 Selected Papers
DOWNLOAD eBooks
Author : China Society of Automotive Engineers
language : en
Publisher: Springer Nature
Release Date :
Proceedings Of China Sae Congress 2023 Selected Papers written by China Society of Automotive Engineers and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.