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Content/Lecturers/Date&Location/Program/Material/Web page/Registration/Questions?


1-day IFAC WC pre-congress workshop


Nonlinear Model Predictive Control:
Introduction and Current Topics

Saturday July 2nd, 2005
directly prior to the  IFAC World Congress 2005 in Prague
Organizer: Rolf Findeisen

Content and objective:

Linear model predictive control is popular since the 70s of the past century and by now widely employed in practice. The 90s have witnessed a steadily increasing attention from control theoreticians as well as control practitioners in the area of nonlinear model predictive control (NMPC) and over the past decade significant theoretical as well as implementational advances in the area of NMPC have been achieved. The focus of this workshop/mini course is twofold. Besides an in depth introduction to the basic ideas and principles of (nonlinear) predictive control current application and research issues in NMPC spanning from stability and robustness, output-feedback, efficient numerical solution as well as implementation aspects are discussed. For this purpose the course is split up in six parts. The first part provides an introduction as well as a historical review of (nonlinear) predictive control, often also referred to as receding horizon control or moving horizon control. Part two focuses on how to achieve nominal stability of the closed-loop using NMPC. In part three the robustness as well as the robust design of NMPC are discusses. Part four provides an overview on output-feedback in conjunction with NMPC. The efficient numerical solution and implementation of NMPC is discussed in depth in part five. Part six discusses existing applications as well as application aspects of NMPC. The workshop/mini course is concluded by a short wrap up, summary and outlook. 

The course 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.

Lecturers:

Details about the lecturers can be found following the links.

Date and location:

The one day workshop/mini course will be held at the Czech Technical University in Prague on Saturday 2nd 2005. See www.feld.cvut.cz.

Detailed program:


 Lecture Content
 Lecturer
 Lecture Details
09:00-10:00
 Introduction and history of predictive control

Frank Allgöwer The main focus in this lecture is laid on an introduction and historical perspective of (nonlinear) predictive control. Specifically we outline the basic principle of predictive control, reasons for the huge success of linear model predictive control and the key advantages, disadvantages and challenges inherent in NMPC.
10:00-10:30
 Basic theory and stability of NMPC I Rolf Findeisen
 Nonlinear model predictive control is based on the  repeated solution of a (finite) horizon open-loop optimal control problem subject to system dynamics and input and state constraints. However, as is well known by now, optimality does not automatically imply stability in the case of finite prediction horizons. Different approaches to achieve closed-loop stability using finite horizon lengths exist. The main purpose of this lecture is to review the underlying main ideas and theoretical foundations for these approaches and to provide a unified view on nominally stabilizing NMPC schemes.
10:30-11:00
 Coffee Break
11:00-11:30 Basic theory and stability of NMPC II Rolf Findeisen Nonlinear model predictive control is based on the  repeated solution of a (finite) horizon open-loop optimal control problem subject to system dynamics and input and state constraints. However, as is well known by now, optimality does not automatically imply stability in the case of finite prediction horizons. Different approaches to achieve closed-loop stability using finite horizon lengths exist. The main purpose of this lecture is to review the underlying main ideas and theoretical foundations for these approaches and to provide a unified view on nominally stabilizing NMPC schemes.
11:30-13:00
 Robustness and robust design of NMPC Lalo Magni
 The introduction of uncertainty in the system description raises the question of robustness. In this lecture we present several approaches to the study of robustness. The first is concerned with the robustness analysis of closed-loop systems, designed using a nominal model. The second attempts to achieve robustness in the context of conventional model predictive control by consideration of a min-max open-loop model predictive control. The third one addresses the robustness problem by introducing feedback in the min-max optimal control problem solved on-line.
13:00-14:00
 Lunch break
14:00-15:00
 Output feedback and NMPC Rolf Findeisen
Nonlinear model predictive control is inherently a state feedback control scheme. Often, however, the full state information is not available and a suitable state observer must be used for state estimation.  Since the well known separation principle does not apply for nonlinear systems,  it is not guaranteed that a  combination of a stabilizing NMPC state feedback controller with a stable state estimator does lead to a stabilizing output-feedback control scheme. In this lecture we review results and conditions on output-feedback NMPC schemes that guarantee stability of the closed loop.
15:00-15:30
 Coffee break
15:30-16:45 Efficient numerical implementation of NMPC Moritz Diehl This lecture presents state-of-the-art methods for numerical solution of the optimal  control problems arising in NMPC. After first giving a brief overview of different solution approaches to optimal control we focus on direct shooting methods and collocation, in conjunction with nonlinear programming techniques. In particular, we discuss the direct multiple shooting method, an algorithm suitable for nonlinear  problems with complex constraints that is often used in NMPC applications, and show ways to deal with limited time for on line computation.
16:45-17:45
 Applications and application aspects of NMPC Zoltan K. Nagy
 This lecture provides an introduction in the development of practical model-based control approaches that can be supported in an industrial environment. The importance of the judicious compromise between, modeling, sensors, estimation and optimization are assessed. Academic NMPC approaches are confronted with industrial methods through several example processes with contouring current trends toward potential applications in biotechnology, polymer, pharmaceutical and microelectronics industry.
17:45-18:00
 Wrap up, summary and outlook

Provided material:

Copies of the slides and supplementary material will be provided to the registered participants.

Course Web Page:

http://www.ist.uni-stuttgart.de/~findeise/nmpcifac/

Registration:

Please refer to http://www.guarant.cz/ifac2005/WorkshopsTutorials.aspx for registration. The registration fee set by IFAC is 220,00 EUR.

Questions:

In case of additional questions or requests please feel free to contact:

Rolf Findeisen
Institute for Systems Theory in Engineering
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 (alphabetical order)

  • Frank Allgöwer (IST, University of Stuttgart, Germany)
    Frank Allgöwer Frank Allgöwer is professor in the mechanical engineering department of the University of Stuttgart and director of the Institute for Systems Theory in  Engineering. Besides his interests in predictive control, he is active in the areas of  nonlinear and robust control, identification of nonlinear systems and application of modern systems and control theoretical methods in engineering and biology. He is Editor for the journal Automatica, Associate Editor of the Journal of Process Control and the European Journal of Control and is on the editorial board of several further journals.  He is organizer or co-organizer of several international conferences and has published over 100 scientific articles.

    Selected publications relevant to the workshop:
    • F. Allgöwer and A.Z. Zheng. Nonlinear Model Predictive Control: Assessment and Future Directions for Research. Progress in Systems and Control Series, Birkhäuser Verlag, Basel. 2000.
    • F. Allgöwer, R. Findeisen, and C. Ebenbauer. Nonlinear model predictive control. Encyclopedia for Life Support Systems (EOLSS) article contribution 6.43.16.2, 2003.
    • H. Chen and F. Allgöwer. A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability. Automatica. Vol. 34, issue. 10, S. 1205-1218, 1998.

  • Moritz Diehl (IWR, University of Heidelberg, Germany)
    Moritz Diehl is mathematics lecturer at the Interdisciplinary Center for Scientific Computing (IWR) of the University of Heidelberg. 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". moritz diehl

    Selected publications relevant to the workshop:
    • M. Diehl, H.G. Bock, J.P. Schlöder, R. Findeisen, Z. Nagy, and F. Allgöwer: Real-time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations . Journal of Process Control 12, pp. 577-585, 2002.
    • 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.

  • Rolf Findeisen (IST, University of Stuttgart, Germany)
    Rolf Findeisen Rolf Findeisen is researcher 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, F. Allgöwer, and C. Ebenbauer. Nonlinear model predictive control. Encyclopedia for
      Life Support Systems (EOLSS) article contribution 6.43.16.2. 2005.
    • M. Diehl, R. Findeisen, and F. Allgöwer. A stabilizing real-time implementation of nonlinear model
      predictive control. In L. Biegler, O. Ghattas, M. Heinkenschloss, D. Keyes, and B. van
      Bloem Wanders, editors, Real-Time PDE Optimization, New York, 2005. Springer-Verlag. To appear.
    • 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.
    • R. Findeisen, L. Imsland, F. Allgöwer, and B.A. Foss. State and output feedback nonlinear model predictive control: An overview. Europ. J. Contr., 9(2-3):190-207, 2003.
    • R. Findeisen, L. Imsland, F. Allgöwer, and B.A. Foss. Towards a sampled-data theory for nonlinear model predictive control. In C. Kang, M. Xiao, and W. Borges, editors, New Trends in Nonlinear Dynamics and Control, and their Applications, Lecture Notes in Control and Information Sciences, 295, pages 295-313, New York, 2003. Springer-Verlag.

  • Lalo Magni (SISDIN, University of Pavia, Italy):
    Lalo Magni got his PhD in Electronic and Computer Engineering in 1998 with the dissertation :"Nonlinear Receding Horizon Control: Theory and Application". Currently he is Assistant Professor at the University of Pavia, Italy. His research in model predictive control is witnessed by 20 papers appeared in the main international journals of the field. He has been Guest Editor of the Special Issue "Control of nonlinear systems with Model Predictive Control" in the International Journal of Robust and Nonlinear Control. He serves as an Associate Editor of the IEEE Transactions on Automatic Control. Lalo Magni

    Selected publications relevant to the workshop:
    • Fontes F.A.C.C. and L. Magni, Min-max Model Predictive Control of Nonlinear Systems using Discontinuous Feedbacks, IEEE Transactions on Automatic Control,48,pp. 1750-1755, 2003.
    • Magni L., G. De Nicolao, R. Scattolini and F. Allgöwer, Robust Model predictive Control of nonlinear discrete-time systems, International Journal of Robust and nonlinear control, 13, Issue 3-4, pp. 229-246, 2003.
    • Magni L., H. Nijmeijer and A.J. Van Der Schaft, A receding-horizon approach to the nonlinear H Inf. Control problem, Automatica, 37(3), pag. 429-435, 2001.

  • Zoltan Nagy (IST, University of Stuttgart, Germany):
    Zoltan K. Nagy
    Zoltan K. Nagy received his Ph.D. in chemical engineering, from the "Babes-Bolyai" University of Cluj, Romania in 2001, where he holds a lecturer position. In 2001-2003 he was a research associate and lecturer at the University of Illinois at Urbana-Champaign, USA. He is currently with the University of Stuttgart, working on an industrial project with BASF and ABB related to a feasibility study of industrial NMPC. His research interests include: nonlinear model predictive control, batch process control, uncertainty analysis, robust optimal control, mathematical modeling of chemical processes. He received the outstanding reviewer award for Automatica in 2003, and serves as reviewer for McGraw-Hill and for several journals and conferences including, Journal of Process Control, IEE Proceedings on Control Theory and Applications, Chemical Engineering Communications.

    Selected publications relevant to the workshop:
    • Z. K. Nagy and R. D. Braatz, Robust nonlinear model predictive control of batch processes, AIChE J., 49 (7), 1776-1786, 2003.
    • Z. K. Nagy, R. D. Braatz, Worst-case and Distributional Robustness Analysis of Finite-time Control Trajectories for Nonlinear Distributed Parameter Systems, IEEE Transaction on Control Systems Technology, 11 (5), 694-704, 2003.
    • Z. K. Nagy and R. D. Braatz, Open-loop and closed-loop robust optimal control of batch processes using distributional and worst-case analysis, Journal of Process Control, 14, 411-422, 2004.





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