Download or read book Relaxed Barrier Function Based Model Predictive Control written by Christian Feller and published by Logos Verlag Berlin GmbH. This book was released on 2017 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we introduce the novel concept of relaxed barrier function based model predictive control and present a comprehensive theoretical and algorithmic framework for the design, analysis, and implementation of relaxed barrier function based MPC approaches. Instead of treating the underlying optimization as an idealized static map, a key motive of the MPC results and algorithms presented in this thesis is to study the interconnected dynamics of controlled plant and iterative optimization algorithm in an integrated barrier function based framework and to analyze the resulting overall closed-loop system both from a systems theoretic and algorithmic perspective. One of the presented main results is a novel class of barrier function based anytime MPC algorithms that guarantee important properties of the closed-loop system independently of the number of optimization algorithm iterations that are performed at each sampling step. The obtained theoretical results are illustrated by various numerical examples and benchmark tests as well as by an experimental case study in which the proposed class of barrier function based MPC algorithms is applied to the predictive control of a self-driving car.
Download or read book Trajectory tracking path following and learning in model predictive control written by Fabian Russell Pfitz and published by Logos Verlag Berlin GmbH. This book was released on 2023-08-21 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book 12th International Munich Chassis Symposium 2021 written by Peter Pfeffer and published by Springer Nature. This book was released on 2022-04-18 with total page 784 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increasing automation of driving functions and the electrification of powertrains present new challenges for the chassis with regard to complexity, redundancy, data security, and installation space. At the same time, the mobility of the future will also require entirely new vehicle concepts, particularly in urban areas. The intelligent chassis must be connected, electrified, and automated in order to be best prepared for this future. Contents Driving Simulators.- Innovative Chassis Systems.- Automated Driving and Racing.- New Methods and Systems.- Suspension and Ride Comfort.- All-Wheel Steering.- Future Brake Systems and Testing Technology.- Innovations in Tires and Wheels. Target audiences Automotive engineers and chassis specialists as well as students looking for state-of-the-art information regarding their field of activity - Lecturers and instructors at universities and universities of applied sciences with the main subject of automotive engineering - Experts, researchers and development engineers of the automotive and the supplying industry. Publisher ATZ live stands for top quality and a high level of specialist information and is part of Springer Nature, one of the leading publishing groups worldwide for scientific, educational and specialist literature. Partner TÜV SÜD is an international leading technical service organisation catering to the industry, mobility and certification segment.
Download or read book Proximity Moving Horizon Estimation written by Meriem Gharbi and published by Logos Verlag Berlin GmbH. This book was released on 2022-04-01 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we develop and analyze a novel framework for moving horizon estimation (MHE) of linear and nonlinear constrained discrete-time systems, which we refer to as proximity moving horizon estimation. The conceptual idea of the proposed framework is to employ a stabilizing a priori solution in order to ensure stability of MHE and to combine it with an online convex optimization in order to obtain an improved performance without jeopardizing stability. The goal of this thesis is to provide proximity-based MHE approaches with desirable theoretical properties and for which reliable and numerically efficient algorithms allow the estimator to be applied in real-time applications. In more detail, we present constructive and simple MHE design procedures which are tailored to the considered class of dynamical systems in order to guarantee important properties of the resulting estimation error dynamics. Furthermore, we develop computationally efficient MHE algorithms in which a suboptimal state estimate is computed at each time instant after an arbitrary and limited number of optimization algorithm iterations. In particular, we introduce a novel class of anytime MHE algorithms which ensure desirable stability and performance properties of the estimator for any number of optimization algorithm iterations, including the case of a single iteration per time instant. In addition to the obtained theoretical results, we discuss the tuning of the performance criteria in proximity MHE given prior knowledge on the system disturbances and illustrate the theoretical properties and practical benefits of the proposed approaches with various numerical examples from the literature.
Download or read book Design of Distributed and Robust Optimization Algorithms A Systems Theoretic Approach written by Simon Michalowsky and published by Logos Verlag Berlin GmbH. This book was released on 2020-04-17 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization algorithms are the backbone of many modern technologies. In this thesis, we address the analysis and design of optimization algorithms from a systems theoretic viewpoint. By properly recasting the algorithm design as a controller synthesis problem, we derive methods that enable a systematic design of tailored optimization algorithms. We consider two specific classes of optimization algorithms: (i) distributed, and (ii) robust optimization algorithms. Concerning (i), we utilize ideas from geometric control in an innovative fashion to derive a novel methodology that enables the design of distributed optimization algorithms under minimal assumptions on the graph topology and the structure of the optimization problem. Concerning (ii), we employ robust control techniques to establish a framework for the analysis of existing algorithms as well as the design of novel robust optimization algorithms with specified guarantees.
Download or read book Developments in Model Based Optimization and Control written by Sorin Olaru and published by Springer. This book was released on 2015-12-23 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with optimization methods as tools for decision making and control in the presence of model uncertainty. It is oriented to the use of these tools in engineering, specifically in automatic control design with all its components: analysis of dynamical systems, identification problems, and feedback control design. Developments in Model-Based Optimization and Control takes advantage of optimization-based formulations for such classical feedback design objectives as stability, performance and feasibility, afforded by the established body of results and methodologies constituting optimal control theory. It makes particular use of the popular formulation known as predictive control or receding-horizon optimization. The individual contributions in this volume are wide-ranging in subject matter but coordinated within a five-part structure covering material on: · complexity and structure in model predictive control (MPC); · collaborative MPC; · distributed MPC; · optimization-based analysis and design; and · applications to bioprocesses, multivehicle systems or energy management. The various contributions cover a subject spectrum including inverse optimality and more modern decentralized and cooperative formulations of receding-horizon optimal control. Readers will find fourteen chapters dedicated to optimization-based tools for robustness analysis, and decision-making in relation to feedback mechanisms—fault detection, for example—and three chapters putting forward applications where the model-based optimization brings a novel perspective. Developments in Model-Based Optimization and Control is a selection of contributions expanded and updated from the Optimisation-based Control and Estimation workshops held in November 2013 and November 2014. It forms a useful resource for academic researchers and graduate students interested in the state of the art in predictive control. Control engineers working in model-based optimization and control, particularly in its bioprocess applications will also find this collection instructive.
Download or read book Model Predictive Control in the Process Industry written by Eduardo F. Camacho and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.
Download or read book Predictive Control for Linear and Hybrid Systems written by Francesco Borrelli and published by Cambridge University Press. This book was released on 2017-06-22 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: With a simple approach that includes real-time applications and algorithms, this book covers the theory of model predictive control (MPC).
Download or read book Model Predictive Control written by James Blake Rawlings and published by . This book was released on 2017 with total page 770 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book 16th International Symposium on Advanced Vehicle Control written by Giampiero Mastinu and published by Springer Nature. This book was released on with total page 1016 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Process Operational Safety and Cybersecurity written by Zhe Wu and published by Springer Nature. This book was released on 2021-06-09 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is focused on the development of rigorous, yet practical, methods for the design of advanced process control systems to improve process operational safety and cybersecurity for a wide range of nonlinear process systems. Process Operational Safety and Cybersecurity develops designs for novel model predictive control systems accounting for operational safety considerations, presents theoretical analysis on recursive feasibility and simultaneous closed-loop stability and safety, and discusses practical considerations including data-driven modeling of nonlinear processes, characterization of closed-loop stability regions and computational efficiency. The text then shifts focus to the design of integrated detection and model predictive control systems which improve process cybersecurity by efficiently detecting and mitigating the impact of intelligent cyber-attacks. The book explores several key areas relating to operational safety and cybersecurity including: machine-learning-based modeling of nonlinear dynamical systems for model predictive control; a framework for detection and resilient control of sensor cyber-attacks for nonlinear systems; insight into theoretical and practical issues associated with the design of control systems for process operational safety and cybersecurity; and a number of numerical simulations of chemical process examples and Aspen simulations of large-scale chemical process networks of industrial relevance. A basic knowledge of nonlinear system analysis, Lyapunov stability techniques, dynamic optimization, and machine-learning techniques will help readers to understand the methodologies proposed. The book is a valuable resource for academic researchers and graduate students pursuing research in this area as well as for process control engineers. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
Download or read book Minimax Approaches to Robust Model Predictive Control written by Johan Löfberg and published by Linköping University Electronic Press. This book was released on 2003-04-11 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: Controlling a system with control and state constraints is one of the most important problems in control theory, but also one of the most challenging. Another important but just as demanding topic is robustness against uncertainties in a controlled system. One of the most successful approaches, both in theory and practice, to control constrained systems is model predictive control (MPC). The basic idea in MPC is to repeatedly solve optimization problems on-line to find an optimal input to the controlled system. In recent years, much effort has been spent to incorporate the robustness problem into this framework. The main part of the thesis revolves around minimax formulations of MPC for uncertain constrained linear discrete-time systems. A minimax strategy in MPC means that worst-case performance with respect to uncertainties is optimized. Unfortunately, many minimax MPC formulations yield intractable optimization problems with exponential complexity. Minimax algorithms for a number of uncertainty models are derived in the thesis. These include systems with bounded external additive disturbances, systems with uncertain gain, and systems described with linear fractional transformations. The central theme in the different algorithms is semidefinite relaxations. This means that the minimax problems are written as uncertain semidefinite programs, and then conservatively approximated using robust optimization theory. The result is an optimization problem with polynomial complexity. The use of semidefinite relaxations enables a framework that allows extensions of the basic algorithms, such as joint minimax control and estimation, and approx- imation of closed-loop minimax MPC using a convex programming framework. Additional topics include development of an efficient optimization algorithm to solve the resulting semidefinite programs and connections between deterministic minimax MPC and stochastic risk-sensitive control. The remaining part of the thesis is devoted to stability issues in MPC for continuous-time nonlinear unconstrained systems. While stability of MPC for un-constrained linear systems essentially is solved with the linear quadratic controller, no such simple solution exists in the nonlinear case. It is shown how tools from modern nonlinear control theory can be used to synthesize finite horizon MPC controllers with guaranteed stability, and more importantly, how some of the tech- nical assumptions in the literature can be dispensed with by using a slightly more complex controller.
Download or read book Assessment and Future Directions of Nonlinear Model Predictive Control written by Rolf Findeisen and published by Springer. This book was released on 2007-09-08 with total page 644 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thepastthree decadeshaveseenrapiddevelopmentin the areaofmodelpred- tive control with respect to both theoretical and application aspects. Over these 30 years, model predictive control for linear systems has been widely applied, especially in the area of process control. However, today’s applications often require driving the process over a wide region and close to the boundaries of - erability, while satisfying constraints and achieving near-optimal performance. Consequently, the application of linear control methods does not always lead to satisfactory performance, and here nonlinear methods must be employed. This is one of the reasons why nonlinear model predictive control (NMPC) has - joyed signi?cant attention over the past years,with a number of recent advances on both the theoretical and application frontier. Additionally, the widespread availability and steadily increasing power of today’s computers, as well as the development of specially tailored numerical solution methods for NMPC, bring thepracticalapplicabilityofNMPCwithinreachevenforveryfastsystems.This has led to a series of new, exciting developments, along with new challenges in the area of NMPC.
Download or read book Model Predictive Control of Microgrids written by Carlos Bordons and published by Springer Nature. This book was released on 2019-09-12 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book shows how the operation of renewable-energy microgrids can be facilitated by the use of model predictive control (MPC). It gives readers a wide overview of control methods for microgrid operation at all levels, ranging from quality of service, to integration in the electricity market. MPC-based solutions are provided for the main control issues related to energy management and optimal operation of microgrids. The authors present MPC techniques for case studies that include different renewable sources – mainly photovoltaic and wind – as well as hybrid storage using batteries, hydrogen and supercapacitors. Experimental results for a pilot-scale microgrid are also presented, as well as simulations of scheduling in the electricity market and integration of electric and hybrid vehicles into the microgrid. in order to replicate the examples provided in the book and to develop and validate control algorithms on existing or projected microgrids. Model Predictive Control of Microgrids will interest researchers and practitioners, enabling them to keep abreast of a rapidly developing field. The text will also help to guide graduate students through processes from the conception and initial design of a microgrid through its implementation to the optimization of microgrid management. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
Download or read book Real Time Optimization for Large Scale Nonlinear Processes written by Moritz Diehl and published by . This book was released on 2002 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Learning and Hybrid Modelling for Reaction Engineering written by Dongda Zhang and published by Royal Society of Chemistry. This book was released on 2023-12-20 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last decade, there has been a significant shift from traditional mechanistic and empirical modelling into statistical and data-driven modelling for applications in reaction engineering. In particular, the integration of machine learning and first-principle models has demonstrated significant potential and success in the discovery of (bio)chemical kinetics, prediction and optimisation of complex reactions, and scale-up of industrial reactors. Summarising the latest research and illustrating the current frontiers in applications of hybrid modelling for chemical and biochemical reaction engineering, Machine Learning and Hybrid Modelling for Reaction Engineering fills a gap in the methodology development of hybrid models. With a systematic explanation of the fundamental theory of hybrid model construction, time-varying parameter estimation, model structure identification and uncertainty analysis, this book is a great resource for both chemical engineers looking to use the latest computational techniques in their research and computational chemists interested in new applications for their work.
Download or read book Applied Predictive Modeling written by Max Kuhn and published by Springer Science & Business Media. This book was released on 2013-05-17 with total page 595 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
