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Lyapunov-based Economic Model Predictive Control of Stochastic Nonlinear Systems

This work focuses on the design of stochastic Lyapunov-based economic model predictive control (SLEMPC) systems for a broad class of stochastic nonlinear systems with input constraints. Under the assumption of stabilizability of the origin of the stochastic nonlinear system via a stochastic Lyapunov-based control law, an economic model predictive controller is proposed that utilizes suitable constraints based on the stochastic Lyapunov-based controller to ensure economic optimality, recursive feasibility and stability in probability in a well-characterized region of the state-space. A chemical process example is used to illustrate the application of the approach and demonstrate its economic benefits with respect to an existing robust EMPC scheme that treats the disturbances in a deterministic, bounded manner.