Gregor Goebel

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— A new type of parametrizations is proposed which allows to reduce the size of the online optimization in linear MPC. The parametrizations combine a first part ensuring feasibility and asymptotic stability of the closed loop and a second part promoting performance. The performance promoting part is determined a priori offline based on a data mining(More)
— In this paper we propose a method of obtaining and employing state dependent parametrizations in order to reduce the on-line computational load in linear model predictive control (MPC). At the core of our results is the application of a data mining algorithm off-line to obtain a number of suitable parametrizations to approximate solutions of the MPC(More)
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