A Data-Driven Adaptive Economic Model Predictive Control for Changing Economic Targets

This work proposes a data-driven economic model predictive control formulation with tracking features for changing economic targets. An adaptive tuning mechanism, integrated into the control system, dynamically adjusts the controller tuning parameters based on changes of economic targets. The adaptation process leverages inference models built with machine learning techniques. This approach enables the controller to adapt to the current conditions of the system, generating a transient behavior that enhances the overall economic performance. A case study is discussed to corroborate the benefits and properties of the proposed controller.