Nonlinear Model Predictive Control (Progress in Systems and Control Theory)

Cover of: Nonlinear Model Predictive Control (Progress in Systems and Control Theory) |

Published by Birkhäuser Basel .

Written in English

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Subjects:

  • Automatic control engineering,
  • Calculus & mathematical analysis,
  • Mathematics for scientists & engineers,
  • Non-linear science,
  • Technology,
  • Adaptive Control,
  • Technology & Industrial Arts,
  • Science/Mathematics,
  • General,
  • Robotics,
  • Control theory,
  • Differentialgleichungen,
  • Mathematics / General,
  • Mathematics : General,
  • Medical : General,
  • Predictive control,
  • Systemtheorie,
  • Technology / Robotics,
  • Variationsrechnung,
  • Engineering - Mechanical

Edition Notes

Book details

ContributionsFrank Allgöwer (Editor), Alex Zheng (Editor)
The Physical Object
FormatHardcover
Number of Pages472
ID Numbers
Open LibraryOL9772300M
ISBN 103764362979
ISBN 109783764362973

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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.

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. 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. Model Predictive Control (Camacho and Bordons) is good basic book for Implications of model predictive control.

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. ISBN: OCLC Number: Description: vii, pages: illustrations ; 24 cm. Contents: Stability and Robustness of Nonlinear Receding Horizon Control / G.

De Nicolao, L. Magi and R. Scattolini --Nonlinear Model Predictive Control. Nonlinear Model Predictive Control by Lars Grüne and Jürgen Pannek. London: SpringerCited by: Model predictive control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints.

It has been in use in the process industries in chemical plants and oil refineries since the s. In recent years it has also been used in power system balancing models and in power predictive.

The nonlinear model predictive control (NMPC) algorithm is a powerful control technique with many open issues for research. This chapter highlights a few of these issues through a series of process and biosystems case studies.

Control Cited by: The IFAC Conference on Nonlinear Model Predictive Control (NMPC ) aims at bringing together researchers interested and working in the field of MPC, from both academia and industry.

This allows. Book Description A study regarding the capabilities of linear and nonlinear model predictive controllers which are investigated by designing and applying to different nonlinear processes.

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 terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants.

NMPC is interpreted as an approximation of infinite-horizon optimal control. 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 importance for NMPC are covered, namely receding horizon control. A nonlinear model predictive controller computes optimal control moves across the prediction horizon using a nonlinear prediction model, a nonlinear cost function, and nonlinear constraints.

For more. Predictive control strategy 1 A model predictive control law contains the basic components of prediction, optimization and receding horizon implementation. A summary of each of these ingredients is given below. Prediction The future response of the controlled plant is predicted using a dynamic Size: 1MB.This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical.

Economic Nonlinear Model Predictive Control. Model Predictive Control (MPC) can be dated back to the s, and can now be regarded as a mature control method, which has had significant impact on industrial process control.

It is applied in many control Cited by:

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