Cautious H2 optimal control using uncertain Markov parameters identified in closed loop

Systems and Control Letters

Volumn 58, Issue 5, 2009, Pages 378-388

  Dong, Jianfei ^a   Verhaegen, Michel ^a  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Bias; Cautiousness; H2 optimal control; Markov parameters; Probabilistic design; Stochastic uncertainty

Indexed keywords

COST FUNCTIONS; DESIGN; OPTIMIZATION; RANDOM PROCESSES; STOCHASTIC MODELS;

BIAS; CAUTIOUSNESS; H2 OPTIMAL CONTROL; MARKOV PARAMETERS; PROBABILISTIC DESIGN; STOCHASTIC UNCERTAINTY;

UNCERTAINTY ANALYSIS;

EID: 61449090359     PISSN: 01676911     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sysconle.2009.01.007     Document Type: Article

References (20)

1. J. Dong, M. Verhaegen, E. Holweg, Closed-loop subspace predictive control for fault tolerant MPC design, in: Proceedings of the 17th IFAC World Congress, Seoul, South Korea, 2008, pp. 3216-3221
2. W. Favoreel, B. de Moor, M. Gevers, P. van Overschee, Model-free subspace-based LQG design, Technical Report ESAT-SISTA/TR 1998-34, KU Leuven, Leuven, Belgium, 1998
3. W. Favoreel, B. de Moor, M. Gevers, P. van Overschee, Closed-loop model-free subspace-based LQG-design, in: Proceedings of the 7th Mediterranean Conference on Control and Automation, Haifa, Israel, 1999
4. ∞ control, Ph.D. Dissertation, Stanford University, 2001
5. Furuta K., and Wongsaisuwan M. Discrete-time LQG dynamic controller design using plant markov parameters. Automatica 31 (1995) 1317-1324
6. Chiuso A. On the relation between CCA and predictor-based subspace identification. IEEE Transactions on Automatic Control 52 (2007) 1795-1812
7. Chiuso A. The role of vector autoregressive modeling in predictor based subspace identification. Automatica 43 (2007) 1034-1048
8. Åström K.J., and Wittenmark B. Computer Controlled Systems: Theory and Design (1984), Prentice Hall
9. J. Dong, M. Verhaegen, On the equivalence of closed-loop subspace predictive control with LQG, in: Proceedings of the 47th IEEE Conference on Decision and Control, Cancun, Mexico, 2008, pp. 4085-4090
10. In: Calafiore G., and Dabbene F. (Eds). Probabilistic and Randomized Methods for Design Under Uncertainty (2006), Springer-Verlag
11. Campi M.C., and Prandini M. Randomized algorithms for the synthesis of cautious adaptive controllers. Systems & Control Letters 49 (2003) 21-36
12. Kanev S., De Schutter B., and Verhaegen M. An ellipsoid algorithm for probabilistic robust controller design. Systems & Control Letters 49 (2003) 365-375
13. Kanev S., and Verhaegen M. Robustly asymptotically stable finite-horizon MPC. Automatica 42 (2006) 2189-2194
14. Öhrn K., Ahlén A., and Sternad M. A probabilistic approach to multivariable robust filtering and open-loop control. IEEE Transactions on Automatic Control 40 (1995) 405-418
15. Polyak B.T., and Tempo R. Probabilistic robust design with linear quadratic regulators. Systems & Control Letters 43 (2001) 343-353
16. Verhaegen M., and Verdult V. Filtering and System Identification: An Introduction (2007), Cambridge University Press
17. J. Dong, M. Verhaegen, Model free probabilistic design with uncertain markov parameters identified in closed loop, in: Proceedings of the 47th IEEE Conference on Decision and Control, Cancun, Mexico, 2008, pp. 2637-2643
18. Kwon W.H., and Han S. Receding Horizon Control: Model Predictive Control for State Models (2005), Springer-Verlag, London
19. Brewer J.W. Kronecker products and matrix calculus in system theory. IEEE Transactions on Circuits and Systems 25 (1978) 772-781
20. Lasserre J.B. A trace inequality for matrix product. IEEE Transactions on Automatic Control 40 (1995) 1500-1501