@misc{Ordys_Andrzej_W._Dynamic,
 author={Ordys, Andrzej W. and Hangstrup, Mads E. and Grimble, Michael J.},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 abstract={In this paper, the optimal control law is derived for a multi-variable state-space Linear Quadratic Gaussian Predictive Controller (LQGPC). A dynamic performance index is utilized resulting in an optimal steady-state controller. Knowledge of future reference values is incorporated into the controller design and the solution is derived using the method of Lagrange multipliers. It is shown how the well-known GPC controller can be obtained as a special case of the LQGPC controller design.},
 abstract={The important advantage of using the LQGPC framework for designing predictive controllers is that, based on stabilizing properties of LQG control, it enables a systematic approach to selection of the design parameters to yield a stable closed-loop system. The system model considered in this paper can be further extended to also include direct feed-through and knowledge about future external inputs.},
 type={artykuł},
 title={Dynamic algorithm for linear quadratic Gaussian predictive control},
 keywords={state-space design, multi-variable control, linear quadratic Gaussian predictive control, generalized predictive control},
}