Data Driven Economic Model Predictive Control

Masoud Kheradmandi and Prashant Mhaskar
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Masoud Kheradmandi: Department of Chemical Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada
Prashant Mhaskar: Department of Chemical Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada

Mathematics, 2018, vol. 6, issue 4, 1-17

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.

Keywords: Lyapunov-based model predictive control (MPC); subspace-based identification; closed-loop identification; model predictive control; economic model predictive control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2018
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