Model Predictive Control Mpc For Constrained Nonlinear Systems
DOWNLOAD
Download Model Predictive Control Mpc For Constrained Nonlinear Systems PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Model Predictive Control Mpc For Constrained Nonlinear Systems 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
Model Predictive Control For Constrained Nonlinear Systems
DOWNLOAD
Author : Simone Loureiro de Oliveira
language : en
Publisher: vdf Hochschulverlag AG
Release Date : 1996
Model Predictive Control For Constrained Nonlinear Systems written by Simone Loureiro de Oliveira and has been published by vdf Hochschulverlag AG this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.
Explicit Nonlinear Model Predictive Control
DOWNLOAD
Author : Alexandra Grancharova
language : en
Publisher: Springer
Release Date : 2012-03-22
Explicit Nonlinear Model Predictive Control written by Alexandra Grancharova and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-22 with Technology & Engineering categories.
Nonlinear Model Predictive Control (NMPC) has become the accepted methodology to solve complex control problems related to process industries. The main motivation behind explicit NMPC is that an explicit state feedback law avoids the need for executing a numerical optimization algorithm in real time. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation and the possibility to design embedded control systems with low software and hardware complexity. This book considers the multi-parametric Nonlinear Programming (mp-NLP) approaches to explicit approximate NMPC of constrained nonlinear systems, developed by the authors, as well as their applications to various NMPC problem formulations and several case studies. The following types of nonlinear systems are considered, resulting in different NMPC problem formulations: ؠ Nonlinear systems described by first-principles models and nonlinear systems described by black-box models; - Nonlinear systems with continuous control inputs and nonlinear systems with quantized control inputs; - Nonlinear systems without uncertainty and nonlinear systems with uncertainties (polyhedral description of uncertainty and stochastic description of uncertainty); - Nonlinear systems, consisting of interconnected nonlinear sub-systems. The proposed mp-NLP approaches are illustrated with applications to several case studies, which are taken from diverse areas such as automotive mechatronics, compressor control, combustion plant control, reactor control, pH maintaining system control, cart and spring system control, and diving computers.
Model Predictive Control Mpc For Constrained Nonlinear Systems
DOWNLOAD
Author : Simone Loureiro de Oliveira
language : en
Publisher:
Release Date : 1998
Model Predictive Control Mpc For Constrained Nonlinear Systems written by Simone Loureiro de Oliveira and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
Model Predictive Control In The Process Industry
DOWNLOAD
Author : Eduardo F. Camacho
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Model Predictive Control In The Process Industry written by Eduardo F. Camacho 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 2012-12-06 with Technology & Engineering categories.
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.
Model Predictive Control
DOWNLOAD
Author : Basil Kouvaritakis
language : en
Publisher: Springer
Release Date : 2015-12-01
Model Predictive Control written by Basil Kouvaritakis and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-01 with Technology & Engineering categories.
For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplicative and stochastic model uncertainty. The book provides: extensive use of illustrative examples; sample problems; and discussion of novel control applications such as resource allocation for sustainable development and turbine-blade control for maximized power capture with simultaneously reduced risk of turbulence-induced damage. Graduate students pursuing courses in model predictive control or more generally in advanced or process control and senior undergraduates in need of a specialized treatment will find Model Predictive Control an invaluable guide to the state of the art in this important subject. For the instructor it provides an authoritative resource for the construction of courses.
Model Based Predictive Control
DOWNLOAD
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.
Predictive Control For Linear And Hybrid Systems
DOWNLOAD
Author : Francesco Borrelli
language : en
Publisher: Cambridge University Press
Release Date : 2017-06-22
Predictive Control For Linear And Hybrid Systems written by Francesco Borrelli and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-22 with Mathematics categories.
With a simple approach that includes real-time applications and algorithms, this book covers the theory of model predictive control (MPC).
Adaptive Optimal Control
DOWNLOAD
Author : Robert R. Bitmead
language : en
Publisher:
Release Date : 1990
Adaptive Optimal Control written by Robert R. Bitmead and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Computers categories.
Exploring connections between adaptive control theory and practice, this book treats the techniques of linear quadratic optimal control and estimation (Kalman filtering), recursive identification, linear systems theory and robust arguments.
Non Linear Predictive Control
DOWNLOAD
Author : Basil Kouvaritakis
language : en
Publisher: IET
Release Date : 2001-10-26
Non Linear Predictive Control written by Basil Kouvaritakis and has been published by IET this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-10-26 with Mathematics categories.
The 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