Abstract
Model predictive control (MPC) has been a field with considerable research efforts and significant improvements in the algorithms. This has led to a fairly large number of successful industrial applications. However, many small and medium enterprises have not embraced MPC, even though their processes may potentially benefit from this control technology. We tackle one aspect of this issue with the development of a nonlinear model predictive control package NEWCON that will be released as free software. The work details the conceptual design, the control problem formulation and the implementation aspects of the code. A possible application is illustrated with an example of the level and reactor temperature control of a simulated CSTR. Finally, the article outlines future development directions of the NEWCON package.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
- Multiple Shooting
- Model Predictive Control
- Free Software
- Multiple Shooting Formulation
- Constraint Relaxation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Biegler, L. T., and J. E. Cuthrell (1985). “Improved Infeasible Path Optimization for Sequential Modular Simulators—II: The Optimization Algorithm”, Computers & Chemical Engineering, 9(3), 257–267.
Bock, H. G., and K. J. Plitt (1984). “A multiple shooting algorithm for direct solu-tion of optimal control”, In Proc. 9th IFAG World Congress, Budapest, Pergamon Press, 242–247.
Cannon, M. (2004). “Efficient nonlinear model predictive control algorithms”, Annual Reviews in Control, 28(2), 229–237.
Diehl, M., D. B. Leineweber, and A. S. Schäfer (2001). “MUSCOD-II Users’ Man-ual”, IWR Preprint 2001-25, University of Heidelberg.
Diehl, M., R. Findeisen, S. Schwarzkopf, I Uslu, F. Allgöwer, H. G. Bock, and J. P. Schiöder (2003). “An efficient approach for nonlinear model predictive control of large-scale systems. Part II: Experimental evaluation for a distillation column”, Automatisierungstechnik, 51(1), 22–29.
Eaton, J. W. (2002) “GNU Octave Manual”, Network Theory Limited.
Eder, H. H. (2003). “Advanced process control: Opportunities, benefits, and bar-riers”, IEE Computing & Control Engineering, 14(5), 10–15.
Franke, R., and E. Arnorld (1996). “Applying new numerical algorithms to the solution of discreet-time optimal control problems”, In Proc. 2nd IEEE European Workshop on Computer Intensive Methods in Control and Signal Processing, Bu-dapest, 67–72.
Franke, R. (1998). “Omuses a tool for the Optimization of multistage systems and HQP a solver for sparse nonlinear optimization, Version 1.5”, Technical report, Technical University of Ilmenau, Germany.
Hawkins, R. E. (2004). “The economics of Open Source Software for a Competitive Firm”, NetNomics, 6(2), 103–117.
Hindmarsh, A. C, and R. Serban (April 2005). “User Documentation for CVODES v2.2.0”, Technical report UCRL-SM-208111, Center for Applied Scientific Computing Lawrence Livermore National Laboratory.
Ihaka, R., and R. Gentleman (1996). “R: A Language for Data Analysis and Graphics”, Journal of Computational and Graphical Statistics, 5(3), 299–314.
Julier, S. J., and J. K. Uhlmann (2004). “Unscented Filtering and Nonlinear Estimation”, IEEE Transactions on Control Systems Technology, 2(3), 169–182.
Lerner, J., and J. Tirole(2002). “Some Simple Economics of Open Source”, The Journal of Industrial Economics, 50(2), 197–234.
Nagy, Z., and R. D. Braatz(2003). “Robust Nonlinear Model Predictive Control of Batch Processes”, AIChE Journal, 49(7), 1776–1786.
Ohtsuka, T. (2004). “A continuation/GMRES method for fast computation of nonlinear receding horizon control”, Automation, 40(4), 563–574.
Oliveira, N. M. C. (1994). “Constraint Handling and Stability Properties of Model Predictive Control”, PhD thesis, Carnegie Mellon University, Pittsburgh, Penn-sylvania.
Oliveira, N. M. C, and L. T. Biegler (1995). “Newton-type Algorithms for Nonlin-ear Process Control. Algorithm and Stability Results”, Automatica, 31(2), 281–286.
Piela, P. C, T. G. Epperly, K. M. Westerberg, and A. W. Westerberg (1991). “AS-CEND: An Object Oriented Computer Environment for Modeling and Analysis — The Modeling Language”, Computers & Chemical Engineering, 15(1), 53–72.
Qin, S. J., and T. A. Badgwell (2003). “A survey of industrial model predictive control technology”, Control Engineering Practice, 11(7), 733–764.
Romanenko, A. (2003). “Open-source software solutions in chemical process engineering-present status and perspectives”, Proceedings of the ISA EXPO 2003 Technical Conference Houston, USA, 21–23.
Santos, L. O., N. M. C. Oliveira, and L. T. Biegler (1995). “Reliable and Efficient Optimization Strategies for Nonlinear Model Predictive Control”, Proc. of DY-GORD+’95, Helsing0r, Denmark (J. B. Rawlings, Ed.), Elsevier Science, Oxford, 33–38.
Santos, L. O. (2001). “Multivariable Predictive Control of Chemical Processes”, PhD thesis, Faculdade de Ciências e Tecnologia, Universidade de Coimbra, Coim-bra, Portugal.
Santos, L. O., P. A. F. N. A. Afonso, J. A. A. M. Castro, N. M. C. Oliveira, and L. T. Biegler (2001). “On-Line Implementation of Nonlinear MPC: An Experimental Case Study”, Control Engineering Practice, 9, 847–857.
Spedding, V. (2002). “Open Doors for Open Source”, Scientific Computing World, 66, 11–13.
Tenny, M. J., S. J. Wright, and J. B. Rawlings (2004). “Nonlinear model predic-tive control via feasibility-perturbed sequential quadratic programming”, Compu-tational Optimization and Applications, 28(1), 87–121.
Young, R. E., R. D. Bartusiak and R. W. Fontaine (2001). “Evolution of an industrial nonlinear model predictive controller”, In J. B. Rawlings, B. A. Ogunnaike, and J. W. Eaton, editors, Chemical Process Control VI: Sixth International Conference on Chemical Process Control Tucson, Arizona, AIChE Symposium Series, 98(326), 342–351.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Romanenko, A., Santos, L.O. (2007). A Nonlinear Model Predictive Control Framework as Free Software: Outlook and Progress Report. In: Findeisen, R., Allgöwer, F., Biegler, L.T. (eds) Assessment and Future Directions of Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, vol 358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72699-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-72699-9_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72698-2
Online ISBN: 978-3-540-72699-9
eBook Packages: EngineeringEngineering (R0)