Nonlinear Model Predictive Control : Theory and Algorithms. 2nd Edition

663 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Reinforcement learning for control: Performance, stability, and deep approximators (2018, background): “Postoyan et al. (2017) also highlight novel connections between MPC (Grimm et al., 2005; Gr¨une and Pannek, 2016; Borrelli et al., 2017) and RL.”
Data-driven approximation of the Koopman generator: Model reduction, system identification, and control (2019, background): “er the control horizon [t 0;t e], and 2R>0 is a small number penalizing the control cost. In order to achieve a feedback 21 behavior, problem (15) is embedded into a model predictive control (MPC) [50] scheme, where it has to be solved repeatedly over a relatively short horizon while the system (”
Approximation Properties of Receding Horizon Optimal Control (2016, methodology): “In fact, results for this particular setting are much older and widespread in the Receding Horizon/Model Predictive Control literature than for the more general setting discussed before, with the first rigorous performance results in the nonlinear setting dating back at least as far as 1988 [35], se”
Using Nonlinear Model Predictive Control for Dynamic Decision Problems in Economics (2015, methodology): “…in order to obtain a good approximation of the infinite horizon optimal solutions, we want to point out that large horizons increase the dimensionality in (6.23) on the one hand and, on the other hand, may cause numerical problems (as shown in Grüne and Pannek (2011) for the inverted pendulum).”