Abstract
This work focuses on economic model predictive control of nonlinear systems. First, an economic model predictive control algorithm that efficiently handles asynchronous and delayed measurements is presented and its application to a chemical process example is demonstrated. This algorithm uses suitable Lyapunov-based constraints to ensure closed-loop stability for a well-defined set of initial conditions. Second, a distributed economic model predictive control architecture for nonlinear systems is presented. In this architecture, the distributed controllers communicate in a sequential fashion, optimize their inputs through maximizing a plant-wide (global) economic objective function and guarantee practical stability of the closed-loop system.
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