4 edition of Nonlinear Model Predictive Control (Progress in Systems and Control Theory) found in the catalog.
Published
April 26, 2000
by Birkhäuser Basel
.
Written in English
Edition Notes
| Contributions | Frank Allgöwer (Editor), Alex Zheng (Editor) |
| The Physical Object | |
|---|---|
| Format | Hardcover |
| Number of Pages | 472 |
| ID Numbers | |
| Open Library | OL9772300M |
| ISBN 10 | 3764362979 |
| ISBN 10 | 9783764362973 |
Abstract: A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose Cited by: Nonlinear Predictive Control Algorithms with Different Input Sequence Parametrizations Applied for the Quadratic Hammerstein and Volterra Models.- Nonlinear Model Predictive Control Based on Stable Wiener and Hammerstein Models.- III Applications of Nonlinear Predictive Control.- An Overview of Nonlinear Model Predictive Control .
2 Nonlinear model predictive control: issues and applications + Show details-Hide details; p. 33 –58 (26) The nonlinear model predictive control (NMPC) algorithm is a powerful control technique with . Nonlinear 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 Brand: Springer London.
This book covers topics relevant to nonlinear process control including empirical modeling, nonlinear state estimation, differential geometric methods, and nonlinear model predictive control. WARNING: . Nonlinear Model Predictive Control. Nonlinear model predictive control (NMPC) has attracted attention in recent years. The continuation method combined with the generalized minimal residual .
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Model predictive control - Wikipedia. Nonlinear Model Predictive Control is a thorough and rigorous introduction to NMPC for discrete-time and sampled-data systems. NMPC is interpreted as an approximation of infinite-horizon optimal control 1/5(1).
This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing Brand: Springer International Publishing.
About this book 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.
This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing. Nonlinear Model Predictive Control. During the past decade model predictive control (MPC), also referred to as receding horizon control or moving horizon control, has become the preferred control strategy 5/5(1).
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. Nonlinear Model Predictive Control is primarily aimed at academic researchers and practitioners working in control and optimisation but the text is self-contained featuring background material on.
This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems.
NMPC schemes with and without stabilizing Author: Lars Grüne. Nonlinear Model Predictive Control Model predictive control (MPC), also referred to as moving horizon control or receding horizon control, has become an attractive feedback strategy, File Size: KB.
Nonlinear Model Predictive Control is a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. Nonlinear Model Predictive Control is a thorough and rigorous introduction to NMPC for discrete-time and sampled-data systems.
NMPC is interpreted as an approximation of infinite-horizon optimal control. This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing Author: Lars Grüne, Jürgen Pannek.
Buy Nonlinear Model Predictive Control: Theory and Algorithms (Communications and Control Engineering) 2nd ed. by Grüne, Lars, Pannek, Jürgen (ISBN: ) from Amazon's Book.
46 3 Nonlinear Model Predictive Control the control as well as on the state. To this end, we introduce a nonempty state con- straint set X⊆Xand for each x ∈Xwe introduce a nonempty control constraint set File Size: KB. Nonlinear model predictive control (NMPC) is an effective method for optimal operation of batch processes.
Most dynamic models however contain significant uncertainties. It is therefore important to. ear model predictive control schemes on the one hand and numerical algorithms on the other hand; for a comprehensive description of the contents we refer to Sect.
As such, the book is somewhat more theoretical than engineering or application ori-ented monographs on nonlinear model predictive control. Nonlinear Model Predictive Control Theory and Algorithms Springer-Verlag, London, 2nd Edition,XIV, p.
80 illus., ISBN (hardcover), (eBook) Springer website for the book. 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. Nonlinear Model Predictive Control The nonlinear optimal control theory was developed in the ’s and ’s, resulting in powerful characterizations such as the maximum prin- ciple, Athans File Size: KB. P. Dufour, Y. Touré, D. Blanc, P.
LaurentOn nonlinear distributed parameter model predictive control strategy: On-line calculation time reduction and application to an experimental drying process Cited by: Nonlinear Model Predictive Control is primarily aimed at academic researchers and practitioners working in nonlinear control but the text is self-contained featuring background material on 4/5(1).
This book gives an up-to-date assessment of the current state of the art in the new field of nonlinear model predictive control (NMPC). The main topic areas that appear to be of central .
