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Economic model predictive control (EMPC) is a feedback control technique that attemptsto tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first-principles or though system identification techniques. However, in industrial practice, it may be difficult in general to obtain an accurate first-principles model of the process. ii The thesis of Anas Wael Alanqar is approved.

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Economic model predictive control (EMPC) is a control scheme that combines real-time dynamic economic process optimization with the feedback properties of model predictive control (MPC) by replacing the quadratic cost function with a general economic cost function. Almost all the recent work on EMPC involves cost functions that are time invariant (do not explicitly account for time-varying process economics). In the present work, we focus on the development of a Lyapunov-based EMPC (LEMPC) scheme that is formulated with an explicitly time-varying economic cost function. First, the formulation of the proposed two-mode LEMPC is given. Second, closed-loop stability is proven through a theoretical treatment. Last, we demonstrate through extensive closed-loop simulations of a chemical process that the proposed LEMPC can achieve stability with time-varying economic cost as well as improve economic performance of the process over a conventional MPC scheme.