Rigorous solution vs. fast update: Acceptable computational delay in NMPC

Proceedings of the IEEE Conference on Decision and Control

Volumn , Issue , 2011, Pages 5230-5235

  Wolf, Inga J ^a   Wurth, Lynn ^b   Marquardt, Wolfgang ^a  

^a RWTH AACHEN UNIVERSITY   (Germany)

^b BAYER AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; DECISION MAKING; ELECTRIC CURRENT CONTROL; ERRORS; MODEL PREDICTIVE CONTROL; PREDICTIVE CONTROL SYSTEMS;

CLOSED-LOOP PERFORMANCE; COMPUTATIONAL ALGORITHM; COMPUTATIONAL DELAYS; CONTROL TRAJECTORY; NONLINEAR MODEL PREDICTIVE CONTROL; ON-LINE DECISION MAKINGS; OPTIMAL CONTROLS; RIGOROUS SOLUTION;

DELAY CONTROL SYSTEMS;

EID: 84860658509     PISSN: 07431546     EISSN: 25762370     Source Type: Conference Proceeding    
DOI: 10.1109/CDC.2011.6160255     Document Type: Conference Paper

References (18)

1. R. Findeisen and F. Allgöwer, Computational delay in nonlinear model predictive control, in Proc. Int. Symp. Adv. Control of Chemical Processes, IFAC ADCHEM, Paper ID LTD 4.1.5 on CD-ROM, 2004.
2. W.H. Chen, D.J. Ballance and J. O'Reilly, Model predictive control of nonlinear systems: computational burden and stability, IEE Proceedings - Control Theory and Applications, vol. 147(4), 2000, pp. 387-394.
3. M. Alamir, A framework for monitoring control updating period in real-time NMPC schemes, in Nonlinear Model Predicitve Control, L. Magni, D. Raimondo and F. Allgöwer (Ed), Lecture Notes in Control and Information Sciences, Springer, 2009, pp. 433-445.
4. 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, 2005, pp. 1714-1736.
5. D. DeHaan and M. Guay, A real-time framework for model-predictive control of continuous-time nonlinear systems, IEEE Transactions on Automatic Control, vol. 52(11), 2007, pp. 2047-2057.
6. C. Büskens and H. Maurer, Real-time control of an industrial robot under control and state constraints, in Online Optimization of Large Scale Systems, M. Grötschel, S.O. Krumke and J. Rambau (Ed), Springer, 2001, pp. 83-92.
7. L. Würth, R. Hannemann and W. Marquardt, Neighboring-extremal updates for nonlinear model-predictive control and dynamic real-time optimization, Journal of Process Control, vol. 19(8), 2009, pp. 1277-1288.
8. V.M. Zavala and L.T. Biegler, The advanced-step NMPC controller: optimality, stability and robustness, Automatica, vol. 45(1), 2009, pp. 86-93.
9. Y. Wang and S. Boyd, Fast model predictive control using online optimization, IEEE Transactions on Control Systems Technology, vol. 18(2), 2010, pp. 267-278.
10. J.V. Kadam and W. Marquardt, Sensitivity-based solution updates in closed-loop dynamic optimization, in Proceedings of the IFAC DYCOPS, vol. 7, Cambridge, USA, 2004.
11. M. Schlegel, K. Stockmann, T. Binder, and W. Marquardt, Dynamic optimization using adaptive control vector parameterization, Computers & Chemical Engineering, vol. 29(8), 2005, pp. 1731-1751.
12. J. Nocedal and S.J. Wright, Numerical optimization, Springer, 1999.
13. R. Hannemann and W. Marquardt, Continuous and discrete composite adjoints for the Hessian of the Lagrangian in shooting algorithms for dynamic optimization, SIAM Journal on Scientific Computing, vol. 31(6), 2010, pp. 4675-4695.
14. A. V. Fiacco, Introduction to sensitivity and stability analysis in nonlinear programming, Mathematics in science and engineering, Academic Press, vol. 165, 1983.
15. M. Diehl, H.J. Ferreau and N. Haverbeke, Efficient numerical methods for nonlinear MPC and moving horizon estimation, in Nonlinear Model Predictive Control, L. Magni, D. Raimondo and F. Allgöwer (Ed), Lecture Notes in Control and Information Sciences, Springer, 2009, pp. 391-417.
16. C. Kravaris, M. Niemiec, R. Berber and C.B. Brosilow, Nonlinear model-based control of nonminimum-phase processes, in Nonlinear Model Based Process Control, R. Berber and C. Kravaris (Ed), Kluwer Academic Publishers. 1998, pp. 115-141.
17. L. Würth, R. Hannemann and W. Marquardt, A two-layer architecture for economically optimal process control and operation, Journal of Process Control, vol. 21(3), 2011, pp. 311-321.
18. R. Bellman, Dynamic Programming, Adaptive control processes: a guided tour, Princeton University Press, 1957.