Model predictive
control (MPC), also referred to as moving or receding horizon control,
is a control strategy in which the applied input is determined on-line
at the recalculation instant by solving an open-loop optimal control
problem over a fixed prediction horizon into the future. The first part
of the obtained open-loop input signal is implemented until new
measurements become available. Based on the new information the
open-loop optimal control problem is solved again and the whole
procedure is repeated.
Over the past decade significant theoretical as well as
implementational advances in the area of nonlinear model predictive
control (NMPC) have been achieved. By now many issues related to
stability and robustness of NMPC are well understood and NMPC has been
successfully applied in practice to relatively slow processes, mainly
in the process industry.
One of the main remaining questions is how NMPC can be applied to relatively
fast systems such as robotic systems, aerospace systems,
electrical systems, or automotive systems, since the appearing optimal
control problem must be solved in real-time. Over the recent years
significant progress with respect to the application of NMPC to fast
systems has been made. The progress is carried by special NMPC
formulations that allow a fast (approximate) solution of the appearing
optimal control problem, as well as recent advances in the area of
real-time solutions of optimal control problems.
The purpose of this
workshop is twofold. The main objective is to provide an in depth review of the existing
solution approaches for the application of NMPC to fast systems
by some of the key researches in the field. The second objective is to underline these approaches
considering practically
relevant control examples from various areas such as the
control of automotive systems, aerospace systems, or fast chemical
processes.
The
workshop starts with an
elementary level before moving to the more advanced
topics. It is accompanied by copies of the slides
and suplementary material provided by the lecturers. It is of interest
to graduate students, engineers, mathematicians
and researchers, who are interested in becoming familiar with the
application of nonlinear model predictive control to fast systems.
Contributors:
Mazen Alamir,
Laboratoire d'Automatique de Grenoble, France
Francesco Borrelli,
Control Department of
the Università del Sannio, Benevento, Italy
Moritz
Diehl (Optimization in Engineering Center (OPTEC), K.U.Leuven, Belgium)
Martin
Guay,
Department of Chemical Engineering, Queen’s University, Canda
In
case of
additional questions or requests please feel free to contact:
Rolf
Findeisen Institute
for
Systems Theory and Automatic Control University
of
Stuttgart Pfaffenwaldring
9 70550
Stuttgart,
Germany Tel.
+49-711-685-7748 Fax.
+49-711-685-7735 findeise@ist.uni-stuttgart.de
Details
about the lecturers
Mazen Alamir, Laboratoire d'Automatique de
Grenoble, France:
Mazen
Alamir is researcher at the French National Research Center. He
coordinates the Nonlinear Research Group of the Grenoble's Control
Laboratory (LAG). His main research areas are nonlinear model
predictive control, nonlinear receding horizon observers, nonlinear
systems diagnosis with application in Mechatronics, process control,
micro-systems and aerospace. He is Guest Editor of the Special Issue
"NMPC for fast systems" in the International Journal of Robust and
Nonlinear Control and organizer of the "IFAC 2006 Workshop on NMPC for
Fast Systems".
Selected
publications
relevant to the workshop:
Alamir, M.: Stabilization of Nonlinear Systems
Using Receding-Horizon Control Schemes: A Parameterized Approach for
Fast Systems. Lecture Notes in Control and Information Sciences,
Springer, London, ISBN 1-84628-470-8 (2006)
Alamir, M.: New path-generation based
receding-horizon formulation for constrained
stabilization of nonlinear systems Automatica 40, No.4, 647-652 (2004).
Alamir, M. and Marchand,
N. Constrained minimum-time-oriented feedback control for the
stabilization of nonholonomic systems in chained form J. Optimization
Theory Appl. 118, No.2, 229-244 (2003).
Francesco
Borrelli,
Control Department of
the Università del Sannio, Benevento, Italy:
Francesco
Borrelli was born in Milano, Italy in 1974. He received the "Laurea"
degree in computer science engineering in 1998 from the University of
Naples "Federico II", Italy. In 2002 he received the Ph.D. from the
Automatic Control Laboratory at ETH Zurich, Switzerland, advised by
Prof. Manfred Morari. In 2003 he received the ETH Medal for the best
Ph.D. dissertation. He has been a research assistant at the Automatic
Control Laboratory of the ETH Zurich and a contract assistant professor
at the Aerospace and Mechanics Department at the University of
Minnesota, USA. Currently he is an assistant professor at the
"Universita' del Sannio", Benevento, Italy. Francesco Borrelli is a
consultant for Ford Research Laboratories (Dearborn, USA) and for
Honeywell Laboratories (Minneapolis,USA). He is author of the book
"Constrained Optimal Control of Linear and Hybrid Systems" published by
Springer Verlag. He is the winner of the Innovation Prize from the
ElectroSwiss Foundation. His research interests include constrained
optimal control, model predictive control, robust control, parametric
programming, singularly perturbed systems and automotive applications
of automatic control.
Selected
publications
relevant to the workshop:
Borrelli, F., Falcone, P.
Keviczky, T., Asgari, J.
and Hrovat, D.: MPC-based approach to active steering for autonomous
vehicle
systems. International Journal on Vehicle Autonomous Systems , vol. 3 ,
no.
2/3/4 , November 2005 , p. 265-291.
Borrelli, F. and Bemporad, A. and Fodor, M. and
Hrovat, D.: A Hybrid Approach to Traction Control. Accepted for
publication on
the IEEE Transaction on Control System Technology, 2005.
Bemporad
A., F. Borrelli and M. Morari, Min-max
Control of Constrained Uncertain Discrete-Time Linear Systems, IEEE
Transaction
on Automatic Control, Vol. 48, No. 9, September 2003.
Moritz
Diehl
(Optimization in Engineering Center (OPTEC), K.U.Leuven, Belgium):
Moritz
Diehl is professor for optimization in engineering at the newly founded
center for optimization in engineering (OPTEC) at K.U. Leuven. His main
research interests are: algorithms for dynamic optimization, nonlinear
model predictive control, parameter- and state estimation; applications
e.g. in chemical engineering, medicine, robotics, power engineering. He
serves as reviewer for "Automatica", "Automatisierungstechnik",
"Computational Optimization and Applications", "Computers and Chemical
Engineering", "Optimization and Engineering", "Journal of Process
Control".
Selected
publications
relevant to the workshop:
M.
Diehl, R. Findeisen, H.G. Bock, J.P. Schlöder, and
F. Allgöwer. Nominal stability of the real-time iteration
scheme for nonlinear
model predictive control. IEE Control Theory Appl., 152(3):296-308,
2005.
M.
Diehl, H.G. Bock, and J.P. Schlöder: A Real-Time
Iteration Scheme for Nonlinear
Optimization in Optimal Feedback Control.
SIAM Journal on Control and Optimization, Vol 43, No 5,
pp. 1714-1736, 2005.
M. Diehl, R.
Findeisen, S. Schwarzkopf, Ilknur Uslu, F.
Allgöwer, H.G. Bock, E. D. Gilles, J.P. Schröder: An
Efficient
Algorithm for Optimization in Nonlinear Model Predictive Control of
Large-Scale Systems.
Automatisierungstechnik 12/2002 and 1/2003.
M. Diehl, I. Uslu,
S. Schwarzkopf, F. Allgöwer,
H.G.
Bock, R. Findeisen, E.D. Gilles, A. Kienle, J.P. Schlöder, and
E.
Stein: Real-Time Optimization for Large Scale Processes: Nonlinear
Model Predictive Control of a High Purity Distillation Column
In Groetschel, Krumke, Rambau (eds.): Online Optimization of Large
Scale Systems: State of the Art, Springer, 2001.
Martin Guay,
Department of Chemical Engineering, Queen’s University, Canada:
Martin
Guay received his Ph.D. in Chemical Engineering at Queen’s
University in 1996. From 1995 to 1997, he was a research scientist in
Research and Development Center at Dupont Canada Inc. In 1997, he
joined the Department of Chemical and Materials Engineering at the
University of Alberta where he was Assistant Professor until 1999. He
then joined the Department of Chemical Engineering at Queen’s
University where he is Associate Professor. He is currently on leave at
CESAME at l’Université Catholique de Louvain in
Louvain-la-Neuve, Belgium. In 2004, he received the Premier Research
Excellence Award from the Government of Ontario. His research interests
are in the area nonlinear control theory, process control, bioprocess
control and manufacturing systems control.
Selected
publications
relevant to the workshop:
D. DeHaan, M. Guay:
A new real-time method for
nonlinear model predictive control, in Assessment and Future Directions
of
Nonlinear Model Predictive Control, Lecture Notes in Control and
Information Sciences,
R. Findeisen, R. Allgöwer, L. Biegler, ed(s)., Springer, 2006.
D. DeHaan, M. Guay: A
Real-time Framework for Model Predictive Control of Continuous-Time
Nonlinear
Systems, Decision and Control, 2005 and 2005 European Control
Conference.
CDC-ECC '05. 2005, pp 957 – 962.
Adetola, V.
and M. Guay: Nonlinear Receding Horizon
Output-Feedback Control of Sampled-Data Systems, J. Proc. Control 15,
4,
469-480, 2005.
Rolf
Findeisen is Habilitand (equivalent to assistant professor) and
lecturer at the Institute for Systems Theory in Engineering at the
University of Stuttgart. His main research areas are: nonlinear model
predictive control, output feedback control, optimization based control
and state estimation, differential algebraic systems, nonlinear
control, system theoretical methods in biomedical engineering and
biological systems; and the application of these methods in
chemical, biological and mechanical systems. He serves as reviewer for
various journals and conferences including Automatica, IEEE Transaction
on Automatic Control, SIAM Journal on Control and Optimization,
Computers and Chemical Engineering, System and Control Letters, Journal
of Process Control.
Selected
publications
relevant to the workshop:
R.
Findeisen, L.B. Biegler, and F. Allgöwer, editors.
Assessment and Future Directions of Nonlinear Model Predictive Control.
Lecture
Notes in Control and Information Sciences. Springer-Verlag, Berlin,
2006.
D. Mayne, S.V. Rakovi, R. Findeisen, and F.
Allgöwer. Robust output feedback model predictive
control of constrained linear systems. Automatica, 1217-1222(42):7,
2006.
M. Diehl, R. Findeisen, H.G. Bock, J.P.
Schlöder, and
F. Allgöwer. Nominal stability of the real-time iteration
scheme for nonlinear
model predictive control. IEE Control Theory Appl., 152(3):296-308,
2005.
R. Findeisen,
L. Imsland,
F. Allgöwer, and
B.A. Foss.
Output feedback stabilization for constrained systems with nonlinear
model predictive control.
Int. J. of Robust and Nonlinear Control, 13(3-4):211-227, 2003.
Toshiyuki
Ohtsuka
(Department of Mechanical Engineering, Graduate School of Engineering,
Osaka University):
Toshiyuki
Ohtsuka was born in Tokyo, Japan, in 1967. He received his doctoral
degree in aerospace engineering from the Tokyo Metropolitan Institute
of Technology, Tokyo, Japan, in 1995. During 1995–1999, he
was an Assistant Professor at the University of Tsukuba, Ibaraki,
Japan. Since 1999, he has been with the Department of
Computer-Controlled Mechanical Systems at the Graduate School of
Engineering, Osaka University, Osaka, Japan, where he is currently an
Associate Professor. His research interests include nonlinear control
theory with applications to aerospace engineering and mechanical
engineering.
Selected
publications
relevant to the workshop:
Ohtsuka,
T. A Continuation/GMRES Method for Fast
Computation of Nonlinear Receding Horizon Control. Automatica, Vol. 40,
No. 4,
Apr. 2004, pp. 563-574.
Seguchi, H., and Ohtsuka, T.: Nonlinear Receding
Horizon Control of an Underactuated Hovercraft. International Journal
of
Robust and Nonlinear Control, Vol. 13, Nos. 3-4, Mar.-Apr. 2003, pp.
381-398.
Ohtsuka,
T., and Fujii, H.A.: Real-Time Optimization
Algorithm for Nonlinear Receding-Horizon Control. Automatica,
Vol. 33, No. 6, June 1997, pp.
1147-1154.