TY - GEN
A1 - Ławryńczuk, Maciej
A2 - Iacono, Mauro - ed.
A2 - Kołodziej, Joanna - ed.
PB - Zielona Góra: Uniwersytet Zielonogórski
N2 - 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.
N2 - 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.
N2 - 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.
L1 - http://www.zbc.uz.zgora.pl/Content/79090/AMCS_2015_25_4_10.pdf
L2 - http://www.zbc.uz.zgora.pl/Content/79090
KW - process control
KW - model predictive control
KW - nonlinear state-space models
KW - extended Kalman filter
KW - on-line linearization
T1 - Nonlinear state-space predictive control with on-line linearisation and state estimation
UR - http://www.zbc.uz.zgora.pl/dlibra/docmetadata?id=79090
ER -