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Number 4 - December 2015
Volume 25 - 2015
Nonlinear state-space predictive control with on-line linearisation and state estimation
Maciej Ławryńczuk
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.
Keywords
process control, model predictive control, nonlinear state-space models, extended Kalman filter, on-line linearization