Nonlinear predictive control with a Gaussian process model

Lecture Notes in Computer Science

Volumn 3355, Issue , 2005, Pages 185-200

  Kocijan, Juš ^a,b   Murray Smith, Roderick ^c,d  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b UNIVERSITY OF NOVA GORICA   (Slovenia)

^c UNIVERSITY OF GLASGOW   (United Kingdom)

^d NATIONAL UNIVERSITY OF IRELAND   (Ireland)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; IDENTIFICATION (CONTROL SYSTEMS); MATHEMATICAL MODELS; NONLINEAR CONTROL SYSTEMS; OPTIMIZATION; PROBABILITY; PROCESS CONTROL;

BLACK-BOX IDENTIFICATION; DYNAMIC SYSTEMS; GAUSSIAN PROCESS MODELS; NONLINEAR PREDICTIVE CONTROL;

PREDICTIVE CONTROL SYSTEMS;

EID: 24144445116     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-30560-6_8     Document Type: Conference Paper

References (24)

1. Allgöwer F., Badgwell T.A., Qin S.J., Rawlings J.B., Wright S.J., Nonlineax predictive control and moving horizon estimation - an introductory overview, In: Frank, P.M (edt.), Advances in control: highlights of ECC'99, Springer, (1999), 391-449.
2. Allgöwer F., Zheng A. (eds.), Nonlinear Model Predictive Control, Progress in system and control theory, Vol. 26, Birkhäuser Verlag, Basel, (2000).
3. Girard A., Rasmussen C.E., Quinonero Candela, J. and Murray-Smith R., Gaussian Process Priors With Uncertain Inputs & Application to Multiple-Step Ahead Time Series Forecasting, NIPS 15, Vancouver, Canada, MIT Press, (2003).
4. Girard A. and Murray-Smith R., Gaussian Process: Prediction at a noisy input and application to iterative multiple-step ahead forecasting of time-series, In: Switching and Learning in Feedback Systems, Eds R. Murray-Smith, R. Shorten, Springer, 2004.
5. Gregorčič G., Lightbody G., Internal model control based on a Gaussian process prior model, Proceedings of ACC'2003, Denver, CO, (2003), 4981-4986.
6. Johansen T.A., Foss B.A., Sorensen A.V., Non-linear predictive control using local models - applied to a batch fermentation process, Control Eng. Practice, 3(3), (1995), 389-396.
7. Henson M.A., Seborg D.E., Adaptive Nonlinear Control of a pH Neutralization Process, IEEE Trans. Control System Technology, Vol. 2, No. 3, (1994), 169-183.
8. Kavšek-Biasizzo K., Škrjanc I., Matko D., Fuzzy predictive control of highly nonlinear pH process, Computers & chemical engineering, Vol. 21, Supp. 1997, (1997), S613-S618.
9. Kerrigan E.C., Maciejowski J.M., Soft constraints and exact penalty functions in model predictive control, Control 2000 Conference, Cambridge, (2000).
10. Kocijan J., Girard A., Banko B., Murray-Smith R., Dynamic Systems Identification with Gaussian Processes, Proceedings of 4th Mathmod, Vienna, (2003), 776-784.
11. Kocijan J., Likar B., Banko B., Girard A., Murray-Smith R., Rasmussen C.E., A case based comparison of identification with neural network and Gaussian process models, Preprints of IFAC ICONS Conference, Faro, (2003), 137-142.
12. Kouvaritakis B., Cannon M. (eds.), Nonlinear predictive control, Theory and practice, IEE Control Engineering Series 61, IEE, (2001).
13. Maciejowski J.M., Predictive control with constraints, Pearson Education Limited, Harlow, (2002).
14. Murray-Smith, R., Girard, A., Gaussian Process priors with ARMA noise models, Irish Signals and Systems Conference, Maynooth, (2001), 147-152.
15. Murray-Smith, R and T. A. Johansen and R. Shorten, On transient dynamics, off-equilibrium behaviour and identification in blended multiple model structures, European Control Conference, Karlsruhe, (1999), BA-14.
16. Murray-Smith R. and Sbarbaro D., Nonlinear adaptive control using nonparametric Gaussian process prior models, In: Proc. IFAC Congress, Barcelona, (2002).
17. Nørgaard M., Ravn O., Poulsen N.K., Hansen L.K., Neural networks for modelling and control of dynamic systems, Springer, London, (2000).
18. Qin S.J., Badgwell T.A., An overview of industrial model predictive control technology, In: Kantor J.C., Garcia C.E., Carnahan B. (eds.) Fifthe international conference on Chemical process control, AChE and CACHE, (1997), 232-56.
19. Qin S.J., Badgwell T.A., An overview of nonlinear model predictive control applications, In: Allgöwer F., Zheng A. (eds.), Nonlinear model predictive control, Birkhauser Verlag, (2000), 369-392.
20. Rasmussen C.E., Evaluation of Gaussian Processes and other Methods for Non-Linear Regression, Ph.D. Disertation, Graduate department of Computer Science, University of Toronto, Toronto, (1996).
21. Solak, E., Murray-Smith R., Leithead, W.E., Leith, D.J., and Rasmussen, C.E., Derivative observations in Gaussian Process models of dynamic systems, NIPS 15, Vancouver, Canada, MIT Press, (2003).
22. Williams C.K.I., Prediction with Gaussian processes: From linear regression to linear prediction and beyond, In: Learning in Graphical Models, Jordan, M.I., (ed.), Kluwer Academic, Dordrecht, (1998), 599-621.
23. Young R.E., Bartusiak R.D., Fontaine, R.W., Evolution of an industrial nonlinear model predictive controller, Preprints on Chemical Process Control - CPC VI, CACHE, Tucson, AZ, (2001), 399-401.
24. Zheng A., Morari M., Stability of model predictive control with mixed constraints, IEEE Trans. Autom. Control, 40(19), (1995), 1818-1823.