Learning Based Model Predictive Control With Closed Loop Guarantees
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Learning Based Model Predictive Control With Closed Loop Guarantees
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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.
Recent Advances In Model Predictive Control
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Author : Timm Faulwasser
language : en
Publisher: Springer Nature
Release Date : 2021-04-17
Recent Advances In Model Predictive Control written by Timm Faulwasser and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-17 with Science categories.
This book focuses on distributed and economic Model Predictive Control (MPC) with applications in different fields. MPC is one of the most successful advanced control methodologies due to the simplicity of the basic idea (measure the current state, predict and optimize the future behavior of the plant to determine an input signal, and repeat this procedure ad infinitum) and its capability to deal with constrained nonlinear multi-input multi-output systems. While the basic idea is simple, the rigorous analysis of the MPC closed loop can be quite involved. Here, distributed means that either the computation is distributed to meet real-time requirements for (very) large-scale systems or that distributed agents act autonomously while being coupled via the constraints and/or the control objective. In the latter case, communication is necessary to maintain feasibility or to recover system-wide optimal performance. The term economic refers to general control tasks and, thus, goes beyond the typically predominant control objective of set-point stabilization. Here, recently developed concepts like (strict) dissipativity of optimal control problems or turnpike properties play a crucial role. The book collects research and survey articles on recent ideas and it provides perspectives on current trends in nonlinear model predictive control. Indeed, the book is the outcome of a series of six workshops funded by the German Research Foundation (DFG) involving early-stage career scientists from different countries and from leading European industry stakeholders.
Deep Learning For Unmanned Systems
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Author : Anis Koubaa
language : en
Publisher: Springer Nature
Release Date : 2021-10-01
Deep Learning For Unmanned Systems written by Anis Koubaa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-01 with Technology & Engineering categories.
This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets. In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS) The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references. The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques. The book chapters are lucidly illustrated with numerical examples and simulations. The book chapters discuss details of applications and future research areas.
Model Based Predictive Control
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Author : J.A. Rossiter
language : en
Publisher: CRC Press
Release Date : 2003-06-27
Model Based Predictive Control written by J.A. Rossiter and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-27 with Technology & Engineering categories.
Model Predictive Control (MPC) has become a widely used methodology across all engineering disciplines, yet there are few books which study this approach. Until now, no book has addressed in detail all key issues in the field including apriori stability and robust stability results. Engineers and MPC researchers now have a volume that provides a complete overview of the theory and practice of MPC as it relates to process and control engineering. Model-Based Predictive Control, A Practical Approach, analyzes predictive control from its base mathematical foundation, but delivers the subject matter in a readable, intuitive style. The author writes in layman's terms, avoiding jargon and using a style that relies upon personal insight into practical applications. This detailed introduction to predictive control introduces basic MPC concepts and demonstrates how they are applied in the design and control of systems, experiments, and industrial processes. The text outlines how to model, provide robustness, handle constraints, ensure feasibility, and guarantee stability. It also details options in regard to algorithms, models, and complexity vs. performance issues.
Trajectory Tracking Path Following And Learning In Model Predictive Control
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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.
Model Predictive Control
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Author : James Blake Rawlings
language : en
Publisher:
Release Date : 2017
Model Predictive Control written by James Blake Rawlings and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Control theory categories.
Polynomial Approximation For Data Driven System Analysis And Control Of Nonlinear Systems
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Author : Tim Martin
language : en
Publisher: Logos Verlag Berlin GmbH
Release Date : 2024-12-15
Polynomial Approximation For Data Driven System Analysis And Control Of Nonlinear Systems written by Tim Martin 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 2024-12-15 with Technology & Engineering categories.
This thesis presents data-driven methods for nonlinear systems, enabling the verification of system-theoretical properties and the design of state feedbacks based on measured trajectories. Despite noisy data, the developed methods provide rigorous guarantees and leverage convex optimization. Classical control techniques require a mathematical model of the system dynamics, which derivation from first principles often demands expert knowledge or is time-consuming. In contrast, data-based control methods determine system properties and controllers from system trajectories. Whereas recent developments address linear systems, dynamical systems are generally nonlinear in practice. Therefore, this thesis first introduces a data-based system representation for unknown polynomial systems to determine dissipativity and integral quadratic constraints via sum-of-squares optimization. The second part of the thesis establishes a polynomial representation of nonlinear systems based on polynomial interpolation. Due to the unknown interpolation polynomial, a set of polynomials containing the actual interpolation polynomial is deduced from noisy data. This set, along with a polynomial bound on the approximation error, forms the basis for determining dissipativity properties and designing state feedbacks with stability guarantees utilizing robust control techniques and sum-of-squares relaxation.
Algorithmic Foundations Of Robotics Xiv
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Author : Steven M. LaValle
language : en
Publisher: Springer Nature
Release Date : 2021-02-08
Algorithmic Foundations Of Robotics Xiv written by Steven M. LaValle and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-08 with Technology & Engineering categories.
This proceedings book helps bring insights from this array of technical sub-topics together, as advanced robot algorithms draw on the combined expertise of many fields—including control theory, computational geometry and topology, geometrical and physical modeling, reasoning under uncertainty, probabilistic algorithms, game theory, and theoretical computer science. Intelligent robots and autonomous systems depend on algorithms that efficiently realize functionalities ranging from perception to decision making, from motion planning to control. The works collected in this SPAR book represent the state of the art in algorithmic robotics. They originate from papers accepted to the 14th International Workshop on the Algorithmic Foundations of Robotics (WAFR), traditionally a biannual, single-track meeting of leading researchers in the field of robotics. WAFR has always served as a premiere venue for the publication of some of robotics’ most important, fundamental, and lasting algorithmic contributions, ensuring the rapid circulation of new ideas. Though an in-person meeting was planned for June 15–17, 2020, in Oulu, Finland, the event ended up being canceled owing to the infeasibility of international travel during the global COVID-19 crisis.
Advances In Dynamics Of Vehicles On Roads And Tracks Iii
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Author : Wei Huang
language : en
Publisher: Springer Nature
Release Date : 2024-10-12
Advances In Dynamics Of Vehicles On Roads And Tracks Iii written by Wei Huang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-12 with Technology & Engineering categories.
This book offers a timely snapshot of research and development in road vehicle dynamics. Gathering a set of peer-reviewed contributions to the 28th Symposium of the International Association of Vehicle System Dynamics (IAVSD), which was held on August 21–25, 2023 in Ottawa, Canada, this second volume of the proceedings covers a broad range of topics related to on- and off-road vehicles. Topics covered include modelling and simulation, design, control, performance monitoring, and autonomous driving. The papers in this volume also discuss strategies to improve safety, performance, and ride comfort, among others. Overall, this book provides academics and professionals with a timely reference on state-of-the-art theories and methods that can be used to understand, analyze, and improve on- and off-road vehicle safety and performance in a wide range of operating conditions.
Control Of Variable Geometry Vehicle Suspensions
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Author : Balázs Németh
language : en
Publisher: Springer Nature
Release Date : 2023-07-08
Control Of Variable Geometry Vehicle Suspensions written by Balázs Németh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-08 with Technology & Engineering categories.
This book provides a thorough and fresh treatment of the control of innovative variable-geometry vehicle suspension systems. A deep survey on the topic, which covers the varying types of existing variable-geometry suspension solutions, introduces the study. The book discusses three important aspects of the subject: • robust control design; • nonlinear system analysis; and • integration of learning and control methods. The importance of variable-geometry suspensions and the effectiveness of design methods implemented in the autonomous functionalities of electric vehicles—functionalities like independent steering and torque vectoring—are illustrated. The authors detail the theoretical background of modeling, control design, and analysis for each functionality. The theoretical results achieved through simulation examples and hardware-in-the-loop scenarios are confirmed. The book highlights emerging ideas of applying machine-learning-based methods in the control system with guarantees on safety performance. The authors propose novel control methods, based on the theory of robust linear parameter-varying systems, with examples for various suspension systems. Academic researchers interested in automotive systems and their counterparts involved in industrial research and development will find much to interest them in the eleven chapters of Control of Variable-Geometry Vehicle Suspensions.