Economic model predictive control of nonlinear process systems using Lyapunov techniques

By Mohsen Heidarinejad, Jinfeng Liu, Panagiotis D. Christofides. 2012

386 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/57486f4ad7a4edc37cb7d888ce66ef39dbcfdf30

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In Control and Systems Engineering

In Molecular Biology

Citations in the corpus

Single-layer economic model predictive control for periodic operation (2014, background): “In [10, 11, 12], Lyapunov based model predictive control designs, which are capable of optimizing closed-loop performance with respect to general economic considerations for a broad class of nonlinear process systems, including systems subject to asynchronous and delayed measurements and uncertain v”
A tutorial review of economic model predictive control methods (2014, background): “4 (see [68] for more details regarding the controller design).”
Non-linear economic model predictive control of water distribution networks (2017, methodology): “The stability and cost improvement of EMPC have been investigated for non-linear processes in [3, 4], where Lyapunov-based 30 technique has been employed over the conventional MPC.”
On the role of dissipativity in economic model predictive control (2015, background): “, (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.”
Fundamentals of economic model predictive control (2012, methodology): “The design of economic model predictive controllers using Lyapunov techniques is presented in [28] and an analysis of performance in the absence of any terminal constraints or penalties is presented in [29].”
Distributed model predictive control: A tutorial review and future research directions (2013, neutral): “…setting and accommodate input/state constraints, distributed model predictive control (MPC) framework can be employed, particularly in cases where the computational complexity of a centralized MPC may signiﬁcantly increase as the number of operational modes, control inputs and states increases.”