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Mathematics, Volume 6, Issue 4 (April 2018)
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Cover Story (view full-size image) We investigated the problem of a data-driven, model-based economic model predictive control (MPC) [...] Read more. We investigated the problem of a 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 operations 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 the example of a nonlinear chemical process system. View this paper