<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Nonlinear State–Space Predictive Control with On–Line Linearisation and State Estimation

Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper describes computationally efficient model predictive control (MPC) algorithms for nonlinear dynamic systems represented by discrete-time state-space...

Nonlinear State–Space Predictive Control with On–Line Linearisation and State Estimation

Published Online: Dec 30, 2015

Page range: 833 - 847

Received: Jul 10, 2014

DOI: https://doi.org/10.1515/amcs-2015-0060

Keywords
process control, model predictive control, nonlinear state-space models, extended Kalman filter, on-line linearisation

© 2015 Maciej Ławryńczuk, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Nonlinear State–Space Predictive Control with On–Line Linearisation and State Estimation

Published Online: Dec 30, 2015

Page range: 833 - 847

Received: Jul 10, 2014

DOI: https://doi.org/10.1515/amcs-2015-0060

Keywordsprocess control, model predictive control, nonlinear state-space models, extended Kalman filter, on-line linearisation

© 2015 Maciej Ławryńczuk, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
process control, model predictive control, nonlinear state-space models, extended Kalman filter, on-line linearisation