Approximate Scenario-Based Economic Model Predictive Control With Application to Wind Energy Conversion System

Abstract

This paper considers the effective handling of uncertainty for economic model predictive control with feasibility and stability guarantees. Firstly, a stable scenario-based economic model predictive control strategy is proposed based on Lyapunov techniques. This control strategy optimizes over a sequence of control policies instead of a sequence of control inputs, so as to take feedback into account to reduce the conservativeness. More uncertainty information over the prediction horizon is incorporated by employing an augmented prediction model with a scenario tree describing the evolution of the uncertainty. Secondly, since the scenario tree structure inevitably increases the optimization problem size, a trained deep neural network, as an approximation function, is resorted to modeling the scenario-based economic model predictive control feedback control law to make online implementation tractable. The effectiveness of this approximate controller is verified through the probabilistic validation technique. Finally, the feasibility and stability of this approximate scenario-based economic model predictive control are addressed theoretically. An application of this proposed controller on wind energy conversion systems demonstrates its effectiveness.