Nonlinear Model Predictive Control

462 Pages·2000·18.398 MB·English

by  G. De Nicolao, L. Magni, R. Scattolini (auth.), Frank Allgöwer, Alex Zheng (eds.)

Progress in Systems and Control Theory
Volume 26
Series Editor
Christopher I. Byrnes, Washington University
Nonlinear Model
Predictive Control
Frank Allgöwer
Alex Zheng
Editors
Springer Basel AG
Editors:
Frank Allgöwer Alex Zheng
Institut für Systemtheorie technischer Prozesse Department of Chemical Engineering
Universität Stuttgart University of Massachusetts at Amherst
Pfaffenwaldring 9 159 Goessmann Lab
70550 Stuttgart Amherst, MA 01003-3110
Germany USA
1991 Mathematics Subject Classification 93-06, 49-06, 34-06; 34H05, 34K35
A CIP catalogue record for this book is available from the Library of Congress,
Washington D.C, USA
Deutsche Bibliothek Cataloging-in-Publication Data
Nonlinear model predictive control / Frank Allgöwer; Alex Zheng, ed.. - Basel; Boston ;
Berlin; Birkhäuser, 2000
(Progress in systems and control theory ; Vol. 26)
ISBN 978-3-0348-9554-5 ISBN 978-3-0348-8407-5 (eBook)
DOI 10.1007/978-3-0348-8407-5
ISBN 978-3-0348-9554-5
This work is subject to copyright. All rights are reserved, whether the whole or part of the
material is concerned, specifically the rights of translation, reprinting, re-use of illustrati
ons, broadcasting, reproduction on microfilms or in other ways, and storage in data banks.
For any kind of use whatsoever, permission from the copyright owner must be obtained.
© 2000 Springer Basel AG
Originally published by Birkhäuser Verlag in 2000
Softcover reprint of the hardcover 1st edition 2000
Printed on acid-free paper produced of chlorine-free pulp. TCF °°
ISBN 978-3-0348-9554-5
98765432 1
Contents
Preface ix
Part I
Theoretical Issues in Nonlinear Predictive Control
G. De Nicolao, L. Magi and R. Scattolini
Stability and Robustness of Nonlinear Receding Horizon Control 3
David Mayne
Nonlinear Model Predictive Control:
Challenges and Opportunities 23
Christopher V. Rao and James B. Rawlings
Nonlinear Horizon State Estimation 45
Alberto Bemporad, Manfred Morari
Predictive Control ofConstrained Hybrid Systems 71
Basil Kouvaritakis, Mark Cannon and J. Anthony Rossiter
Stability, Feasibility, Optimality and the Degrees ofFreedom
in Constrained Predictive Control 99
David Angeli, Alessandro Casavola and Edoardo Mosca
A Predictive Command Governor for Nonlinear Systems
under Constraints 115
Alex Zheng
Some Practical Issues and Possible Solutions for
Nonlinear Model Predictive Control 129
RolfFindeisen and Frank Allgower
Nonlinear Model Predictive Control for
Index-one DAE Systems 145
Masoud Soroush and Kenne'th R. Muske
Analytical Predictive Control 163
J.M. Lemos, L.M. Rato and E. Mosca
Integrating Predictive and Switching Control:
Basic Concepts and an Experimental Case Study 181
vi Contents
J.D. Trierweiler and A.R. Secchi
Exploring the Potentiality of Using Multiple Model Approach
in Nonlinear Model Predictive Control 191
Mark Cannon and Basil Kouvaritakis
Continuous-time Predictive Control of
Constrained Nonlinear Systems 205
Part II
Modelling and Computational Aspects in Nonlinear Predictive Control
Lorenz T. Biegler
Efficient Solution ofDynamic Optimization and
NMPC Problems 219
H.G. Bock, M.M. Diehl, D.B. Leineweber, J.P. Schloder
A Direct Multiple Shooting Method for Real-time Optimization
of Nonlinear DAE Processes 245
Jay H. Lee
Modeling and Identification for Nonlinear Model Predictive Control:
Requirements, Current Status and Future Research Needs 269
Achim Helbig, OlafAbel, Wolfgang Marquardt
Structural Concepts for Optimization Based Control of
Transient Processes 295
Michael Nikolaou and Diwakar Mantha
Efficient Nonlinear Modeling Using Wavelet Compression 313
Luigi Chisci, J. Anthony Rossiter and Giovanni Zappa
Iterative Active-set Method for Efficient On-line MPC Design 335
Robert Haber, Ruth Bars and Orsolya Lengyel
Nonlinear Predictive Control Algorithms with Different
Input Sequence Parametrizations Applied for the
Quadratic Hammerstein and Volterra Models 347
Matthias Wellers, Heinrich Rake
Nonlinear Model Predictive Control Based on Stable Wiener
and Hammerstein Models 357
Contents vii
Part III
Applications ofNonlinear Predictive Control
S. Joe Qin and Thomas A. Badgwell
An Overview of Nonlinear Model Predictive
Control Applications 369
Joseph Z. Lu
Multi-zone Control under Enterprise Optimization:
Needs, Challenges and Requirements. . . . . . . . . . . . . . . . . . . . . . ... 393
Matthias Schley, Vinay Prasad, Louis P. Russo and B. Wayne Bequette
Nonlinear Model Predictive Control ofa
Styrene Polymerization Reactor 403
Francis J. Doyle III, Philip A. Wisnewski
Nonlinear Multi-rate MPC with Large Scale Fundamental Models:
Application to a Continuous Kamyr Digester 419
V. Wertz, L. Magni and G. Bastin
Multivariable Control ofCement Mills 433
G. De Nicolao, L. Magni and R. Scattolini
Nonlinear Receding Horizon Control of
Internal Combustion Engines 449
Gordon J. Sutton and Robert R. Bitmead
Performance and Computational Implementation of
Nonlinear Model Predictive Control on a Submarine 461
Preface
This volume is based on the contributions, after a rigorous peer review process,
for a workshop on Nonlinear Model Predictive Control that took place on June
2-6, 1998, in Ascona, Switzerland. The workshop was the first international con
ference solely devoted to Nonlinear Model Predictive Control. Nonlinear Model
Predictive Control is presently viewed as one of the most promising areas in au
tomatic control. This is partly due to the increasing industrial need for advanced
control techniques, that address explicitly the process nonlinearity and operat
ing constraints, and the ever-demanding control performance requirement. Since
the research on Nonlinear Model Predictive Control is at its early stage, many
theoretical and implementation issues remain open and very few industrial ap
plications have been reported. With this workshop we wanted to bring together
internationally recognized researchers to assess the current status and to discuss
future research directions. Awide range ofimportant topics, from problem formu
lation, computations, and algorithms to estimation, modelling, and identification
to closed-loop stability and robustness to applications, is covered. We trust that
this volume will contribute to shaping the future research on Nonlinear Model
Predictive Control.
Thisworkshopwas madepossiblebysignificantfinancial contributionsfrom a
numberofsponsors.WewouldliketoespeciallythanktheCentroStefanoFranscini
of ETH, the Swiss National Science Foundation (Schweizerischer Nationalfonds),
the US National Science Foundation (NSF), and the Swiss Society for Automatic
Control (SGA) for their generous support. We are also indebted to RolfFindeisen
and Alberto Bemporad of the Automatic Control Lab at ETH for the boundless
energy they have put into preparingthe workshop. Last but not the least we want
to expressourgratitudeto the participantsofthe workshop for a most stimulating
event and especially to the authors ofthis volume for allowing us to put together,
as we hope, a high quality book.
FRANK ALLGOWER, ETH ZURICH
ALEX ZHENG, UNIVERSITY OF MASSACHUSETTS-AMHERST
Part I
Theoretical Issues in
Nonlinear Predictive Control
ProgressinSystemsandControlTheory,Vol.26
©2000BirkhiiuserVerlagBaseVSwitzerland
Stability and Robustness ofNonlinear Receding
Horizon Control
G. De Nicolao, L. Magni and R. Scattolini
Abstract. The main design strategies for ensuring stability and robustness of
nonlinear RH (Receding-Horizon) control systems are critically surveyed. In
particular, the following algorithms with guaranteed closed-loop stability of
the equilibrium are considered: the zero-state terminal constraint, the dual
modeRHcontroller,theinfinite-horizonclosed-loopcosting,thequasi-infinite
method, and the contractive constraint. For each algorithm, we analyse and
compare feasibility, performance, and implementation issues. For what con
cernsrobustnessanalysisand design, weconsider: monotonicity-based robust
ness, inverse optimality robustness margins, nonlinear Hoc RH design, and a
new nonlinear RH design with local Hoc recovery.
1. Introduction
Receding-Horizon (RH) control, also known as model predictive control, owes its
success to thesimplicityofits rationale whichis wellsuitedto deal with nonlinear
ities and constraints. In discrete-time, the basic idea is to determine the current
control u(t) by solving a finite-horizon optimization problem over the interval
[t,t+N]. At the next time instant t+1, the newcontrol u(t+1) is found bytrans
lating the optimization horizon and solving a new problem over [t+1,t+N +1].
Being optimization-based, the RH scheme can allow for nonlinearities and con
straintsmuch morestraightforwardlythan othermethods. In viewofthe increased
efficiency of the hardware, there are more and more plants on which RH control
can be implemented by solving the finite-horizon optimization on-line.
Animportant caveatofRH controlis thatclosed-loopstabilityis not guaran
teedfor a genericfinite-horizon cost function, as it waswellillustratedbyBitmead,
Gevers and Wertz [3]. In the linear case, the first stability result was obtained by
complementingthe cost function with a terminal zero-state constraint, see e.g. the
work by Kwon and Pearson [22]. Rawlings and Muske removed, at least for the
stable modes, the need ofequality constraints, by introducing a terminal penalty
equal to the infinite-horizon cost due to zero control [34]. It is interesting to note
that a fairly general stability theory for linear RH control can be developed by
referring to the monotonicity properties of a suitable difference Riccati equation
initialized with the terminal penalty matrix [3].