Nonlinear state-space predictive control with on-line linearisation and state estimation

Maciej Ławryńczuk

International Journal of Applied Mathematics and Computer Science (2015)

  • Volume: 25, Issue: 4, page 833-847
  • ISSN: 1641-876X

Abstract

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This paper describes computationally efficient model predictive control (MPC) algorithms for nonlinear dynamic systems represented by discrete-time state-space models. Two approaches are detailed: in the first one the model is successively linearised on-line and used for prediction, while in the second one a linear approximation of the future process trajectory is directly found on-line. In both the cases, as a result of linearisation, the future control policy is calculated by means of quadratic optimisation. For state estimation, the extended Kalman filter is used. The discussed MPC algorithms, although disturbance state observers are not used, are able to compensate for deterministic constant-type external and internal disturbances. In order to illustrate implementation steps and compare the efficiency of the algorithms, a polymerisation reactor benchmark system is considered. In particular, the described MPC algorithms with on-line linearisation are compared with a truly nonlinear MPC approach with nonlinear optimisation repeated at each sampling instant.

How to cite

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Maciej Ławryńczuk. "Nonlinear state-space predictive control with on-line linearisation and state estimation." International Journal of Applied Mathematics and Computer Science 25.4 (2015): 833-847. <http://eudml.org/doc/275909>.

@article{MaciejŁawryńczuk2015,
abstract = {This paper describes computationally efficient model predictive control (MPC) algorithms for nonlinear dynamic systems represented by discrete-time state-space models. Two approaches are detailed: in the first one the model is successively linearised on-line and used for prediction, while in the second one a linear approximation of the future process trajectory is directly found on-line. In both the cases, as a result of linearisation, the future control policy is calculated by means of quadratic optimisation. For state estimation, the extended Kalman filter is used. The discussed MPC algorithms, although disturbance state observers are not used, are able to compensate for deterministic constant-type external and internal disturbances. In order to illustrate implementation steps and compare the efficiency of the algorithms, a polymerisation reactor benchmark system is considered. In particular, the described MPC algorithms with on-line linearisation are compared with a truly nonlinear MPC approach with nonlinear optimisation repeated at each sampling instant.},
author = {Maciej Ławryńczuk},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {process control; model predictive control; nonlinear state-space models; extended Kalman filter; on-line linearisation},
language = {eng},
number = {4},
pages = {833-847},
title = {Nonlinear state-space predictive control with on-line linearisation and state estimation},
url = {http://eudml.org/doc/275909},
volume = {25},
year = {2015},
}

TY - JOUR
AU - Maciej Ławryńczuk
TI - Nonlinear state-space predictive control with on-line linearisation and state estimation
JO - International Journal of Applied Mathematics and Computer Science
PY - 2015
VL - 25
IS - 4
SP - 833
EP - 847
AB - This paper describes computationally efficient model predictive control (MPC) algorithms for nonlinear dynamic systems represented by discrete-time state-space models. Two approaches are detailed: in the first one the model is successively linearised on-line and used for prediction, while in the second one a linear approximation of the future process trajectory is directly found on-line. In both the cases, as a result of linearisation, the future control policy is calculated by means of quadratic optimisation. For state estimation, the extended Kalman filter is used. The discussed MPC algorithms, although disturbance state observers are not used, are able to compensate for deterministic constant-type external and internal disturbances. In order to illustrate implementation steps and compare the efficiency of the algorithms, a polymerisation reactor benchmark system is considered. In particular, the described MPC algorithms with on-line linearisation are compared with a truly nonlinear MPC approach with nonlinear optimisation repeated at each sampling instant.
LA - eng
KW - process control; model predictive control; nonlinear state-space models; extended Kalman filter; on-line linearisation
UR - http://eudml.org/doc/275909
ER -

References

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