A tutorial review of economic model predictive control methods
By Matthew J. Ellis, Helen Durand, Panagiotis D. Christofides. 2014
729 citations recorded by the source corpus.
Subfields: Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/151a05c7a6bfc6bf36938e42a7e49e9203a3e8d4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Frank Allgöwer
- Matthias Albrecht Müller
- Lars Grüne
- Xin Chen
- Na Li
- David Q. Mayne
- Sigurdur Freyr Hafstein
- Peter Giesl
Citations in the corpus
- GEKKO Optimization Suite (2018, background): “Popular practical implementations of dynamic optimization include model predictive control (MPC) [14] (along with its nonlinear variation NMPC [15] and the economic objective alternative EMPC [16]), moving horizon estimation (MHE) [17] and dynamic real-time optimization (DRTO) [18].”
- Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis (2019, background): “In the control community, studies on the above problem focus on economic model predictive control (EMPC), which is a variant of model predictive control (MPC) with a primary goal on optimizing economic costs [9, 10, 11, 12, 13, 14, 15, 16].”
- On the role of dissipativity in economic model predictive control (2015, background): “…in the literature, using different assumptions and/or additional terminal constraints or cost terms, see, e.g., (Angeli et al., 2012; Amrit et al., 2011; Heidarinejad et al., 2012; Müller et al., 2013b; Grüne, 2013; Ferramosca et al., 2014) and the recent survey article by Ellis et al. (2014).”