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Data Driven Economic Model Predictive Control

Department of Chemical Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada
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Mathematics 2018, 6(4), 51; https://doi.org/10.3390/math6040051
Received: 7 March 2018 / Revised: 21 March 2018 / Accepted: 22 March 2018 / Published: 2 April 2018
(This article belongs to the Special Issue New Directions on Model Predictive Control)
This manuscript addresses the problem of data driven model based economic model predictive control (MPC) design. To this end, first, a data-driven Lyapunov-based MPC is designed, and shown to be capable of stabilizing a system at an unstable equilibrium point. The data driven Lyapunov-based MPC utilizes a linear time invariant (LTI) model cognizant of the fact that the training data, owing to the unstable nature of the equilibrium point, has to be obtained from closed-loop operation or experiments. Simulation results are first presented demonstrating closed-loop stability under the proposed data-driven Lyapunov-based MPC. The underlying data-driven model is then utilized as the basis to design an economic MPC. The economic improvements yielded by the proposed method are illustrated through simulations on a nonlinear chemical process system example. View Full-Text
Keywords: Lyapunov-based model predictive control (MPC); subspace-based identification; closed-loop identification; model predictive control; economic model predictive control Lyapunov-based model predictive control (MPC); subspace-based identification; closed-loop identification; model predictive control; economic model predictive control
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MDPI and ACS Style

Kheradmandi, M.; Mhaskar, P. Data Driven Economic Model Predictive Control. Mathematics 2018, 6, 51. https://doi.org/10.3390/math6040051

AMA Style

Kheradmandi M, Mhaskar P. Data Driven Economic Model Predictive Control. Mathematics. 2018; 6(4):51. https://doi.org/10.3390/math6040051

Chicago/Turabian Style

Kheradmandi, Masoud, and Prashant Mhaskar. 2018. "Data Driven Economic Model Predictive Control" Mathematics 6, no. 4: 51. https://doi.org/10.3390/math6040051

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