Nonlinear Model Predictive Control for Industrial Manufacturing Processes with Reconfigurable Machine Tools
Author: Qiang Zhang
Publisher:
Published: 2019
Total Pages: 0
ISBN-13:
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Changeable and Reconfigurable Manufacturing Systems
Author: Hoda A. ElMaraghy
Publisher: Springer Science & Business Media
Published: 2008-11-23
Total Pages: 414
ISBN-13: 1848820674
DOWNLOAD EBOOK“Changeable and Reconfigurable Manufacturing Systems” discusses key strategies for success in the changing manufacturing environment. Changes can often be anticipated but some go beyond the design range, requiring innovative change enablers and adaptation mechanisms. The book presents the new concept of Changeability as an umbrella framework that encompasses paradigms such as agility, adaptability, flexibility and reconfigurability. It provides the definitions and classification of key terms in this new field, and emphasizes the required physical/hard and logical/soft change enablers. The book presents cutting edge technologies and the latest research, as well as future directions to help manufacturers stay competitive. It contains original contributions and results from senior international experts, together with industrial applications. The book serves as a comprehensive reference for professional engineers, managers, and academics in manufacturing, industrial and mechanical engineering.
Nonlinear Model Predictive Control
Author: Lars Grüne
Publisher: Springer Science & Business Media
Published: 2011-04-11
Total Pages: 364
ISBN-13: 0857295012
DOWNLOAD EBOOKNonlinear Model Predictive Control is a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. NMPC schemes with and without stabilizing terminal constraints are detailed and intuitive examples illustrate the performance of different NMPC variants. An introduction to nonlinear optimal control algorithms gives insight into how the nonlinear optimisation routine – the core of any NMPC controller – works. An appendix covering NMPC software and accompanying software in MATLAB® and C++(downloadable from www.springer.com/ISBN) enables readers to perform computer experiments exploring the possibilities and limitations of NMPC.
Assessment and Future Directions of Nonlinear Model Predictive Control
Author: Rolf Findeisen
Publisher: Springer
Published: 2007-09-08
Total Pages: 644
ISBN-13: 3540726993
DOWNLOAD EBOOKThepastthree 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.
Non-linear Predictive Control
Author: Basil Kouvaritakis
Publisher: Institution of Engineering & Technology
Published: 2001-10-26
Total Pages: 288
ISBN-13:
DOWNLOAD EBOOKThe advantage of model predictive control is that it can take systematic account of constraints, thereby allowing processes to operate at the limits of achievable performance. Engineers in academia, industry, and government from the US and Europe explain how the linear version can be adapted and applied to the nonlinear conditions that characterize the dynamics of most real manufacturing plants. They survey theoretical and practical trends, describe some specific theories and demonstrate their practical application, derive strategies that provide appropriate assurance of closed-loop stability, and discuss practical implementation. Annotation copyrighted by Book News, Inc., Portland, OR
Methods of Model Based Process Control
Author: R. Berber
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 814
ISBN-13: 9401101353
DOWNLOAD EBOOKModel based control has emerged as an important way to improve plant efficiency in the process industries, while meeting processing and operating policy constraints. The reader of Methods of Model Based Process Control will find state of the art reports on model based control technology presented by the world's leading scientists and experts from industry. All the important issues that a model based control system has to address are covered in depth, ranging from dynamic simulation and control-relevant identification to information integration. Specific emerging topics are also covered, such as robust control and nonlinear model predictive control. In addition to critical reviews of recent advances, the reader will find new ideas, industrial applications and views of future needs and challenges. Audience: A reference for graduate-level courses and a comprehensive guide for researchers and industrial control engineers in their exploration of the latest trends in the area.
Nonlinear Industrial Control Systems
Author: Michael J. Grimble
Publisher: Springer Nature
Published: 2020-05-19
Total Pages: 778
ISBN-13: 1447174577
DOWNLOAD EBOOKNonlinear Industrial Control Systems presents a range of mostly optimisation-based methods for severely nonlinear systems; it discusses feedforward and feedback control and tracking control systems design. The plant models and design algorithms are provided in a MATLAB® toolbox that enable both academic examples and industrial application studies to be repeated and evaluated, taking into account practical application and implementation problems. The text makes nonlinear control theory accessible to readers having only a background in linear systems, and concentrates on real applications of nonlinear control. It covers: different ways of modelling nonlinear systems including state space, polynomial-based, linear parameter varying, state-dependent and hybrid; design techniques for nonlinear optimal control including generalised-minimum-variance, model predictive control, quadratic-Gaussian, factorised and H∞ design methods; design philosophies that are suitable for aerospace, automotive, marine, process-control, energy systems, robotics, servo systems and manufacturing; steps in design procedures that are illustrated in design studies to define cost-functions and cope with problems such as disturbance rejection, uncertainties and integral wind-up; and baseline non-optimal control techniques such as nonlinear Smith predictors, feedback linearization, sliding mode control and nonlinear PID. Nonlinear Industrial Control Systems is valuable to engineers in industry dealing with actual nonlinear systems. It provides students with a comprehensive range of techniques and examples for solving real nonlinear control design problems.
New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes
Author: Luis Norberto López de Lacalle
Publisher: MDPI
Published: 2020-03-18
Total Pages: 428
ISBN-13: 3039282905
DOWNLOAD EBOOKModern factories are experiencing rapid digital transformation supported by emerging technologies, such as the Industrial Internet of things (IIOT), industrial big data and cloud technologies, deep learning and deep analytics, AI, intelligent robotics, cyber-physical systems and digital twins, complemented by visual computing (including new forms of artificial vision with machine learning, novel HMI, simulation, and visualization). This is evident in the global trend of Industry 4.0. The impact of these technologies is clear in the context of high-performance manufacturing. Important improvements can be achieved in productivity, systems reliability, quality verification, etc. Manufacturing processes, based on advanced mechanical principles, are enhanced by big data analytics on industrial sensor data. In current machine tools and systems, complex sensors gather useful data, which is captured, stored, and processed with edge, fog, or cloud computing. These processes improve with digital monitoring, visual data analytics, AI, and computer vision to achieve a more productive and reliable smart factory. New value chains are also emerging from these technological changes. This book addresses these topics, including contributions deployed in production, as well as general aspects of Industry 4.0.
Nonlinear Model Predictive Control for Industrial Applications
Author: Luisella Balbis
Publisher:
Published: 2007
Total Pages: 0
ISBN-13:
DOWNLOAD EBOOKNetworked and Distributed Predictive Control
Author: Panagiotis D. Christofides
Publisher: Springer Science & Business Media
Published: 2011-04-07
Total Pages: 253
ISBN-13: 0857295829
DOWNLOAD EBOOKNetworked and Distributed Predictive Control presents rigorous, yet practical, methods for the design of networked and distributed predictive control systems – the first book to do so. The design of model predictive control systems using Lyapunov-based techniques accounting for the influence of asynchronous and delayed measurements is followed by a treatment of networked control architecture development. This shows how networked control can augment dedicated control systems in a natural way and takes advantage of additional, potentially asynchronous and delayed measurements to maintain closed loop stability and significantly to improve closed-loop performance. The text then shifts focus to the design of distributed predictive control systems that cooperate efficiently in computing optimal manipulated input trajectories that achieve desired stability, performance and robustness specifications but spend a fraction of the time required by centralized control systems. Key features of this book include: • new techniques for networked and distributed control system design; • insight into issues associated with networked and distributed predictive control and their solution; • detailed appraisal of industrial relevance using computer simulation of nonlinear chemical process networks and wind- and solar-energy-generation systems; and • integrated exposition of novel research topics and rich resource of references to significant recent work. A full understanding of Networked and Distributed Predictive Control requires a basic knowledge of differential equations, linear and nonlinear control theory and optimization methods and the book is intended for academic researchers and graduate students studying control and for process control engineers. The constant attention to practical matters associated with implementation of the theory discussed will help each of these groups understand the application of the book’s methods in greater depth.
