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Mathematics 2018, 6(4), 51; https://doi.org/10.3390/math6040051

Data Driven Economic Model Predictive Control

Department of Chemical Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada
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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)
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Abstract

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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Kheradmandi, M.; Mhaskar, P. Data Driven Economic Model Predictive Control. Mathematics 2018, 6, 51.

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