Explicit Nonlinear Model Predictive Control

Author: Alexandra Grancharova
Editor: Springer Science & Business Media
ISBN: 3642287794
File Size: 22,18 MB
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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.
Nonlinear Model Predictive Control
Language: en
Pages: 576
Authors: Lalo Magni, Davide Martino Raimondo, Frank Allgöwer
Categories: Technology & Engineering
Type: BOOK - Published: 2009-05-25 - Publisher: Springer Science & Business Media

Over the past few years significant progress has been achieved in the field of nonlinear model predictive control (NMPC), also referred to as receding horizon control or moving horizon control. More than 250 papers have been published in 2006 in ISI Journals. With this book we want to bring together
Explicit Nonlinear Model Predictive Control
Language: en
Pages: 234
Authors: Alexandra Grancharova, Tor Arne Johansen
Categories: Technology & Engineering
Type: BOOK - Published: 2012-03-23 - Publisher: Springer Science & Business Media

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,
Automotive Model Predictive Control
Language: en
Pages: 290
Authors: Luigi Del Re, Frank Allgöwer, Luigi Glielmo, Carlos Guardiola, Ilya Kolmanovsky
Categories: Technology & Engineering
Type: BOOK - Published: 2010-03-11 - Publisher: Springer Science & Business Media

Automotive control has developed over the decades from an auxiliary te- nology to a key element without which the actual performances, emission, safety and consumption targets could not be met. Accordingly, automotive control has been increasing its authority and responsibility – at the price of complexity and di?cult tuning. The
Model Predictive Control
Language: en
Pages: 405
Authors: Eduardo F. Camacho, Carlos Bordons Alba
Categories: Technology & Engineering
Type: BOOK - Published: 2013-01-10 - Publisher: Springer Science & Business Media

The second edition of "Model Predictive Control" provides a thorough introduction to theoretical and practical aspects of the most commonly used MPC strategies. It bridges the gap between the powerful but often abstract techniques of control researchers and the more empirical approach of practitioners. The book demonstrates that a powerful
Model Predictive Control
Language: en
Pages: 384
Authors: Basil Kouvaritakis, Mark Cannon
Categories: Technology & Engineering
Type: BOOK - Published: 2015-12-01 - Publisher: Springer

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