Data-driven adaptive two-degree of freedom control of interconnected systems for reference tracking

Abstract

Many industrial processes consist of a series of interconnected components. While the components are often individually controllable, the potentially complex interaction mechanisms limit the design options for a process-wide controller. Typical reasons are that parts of the component interactions are difficult to model or that the overall, detailed physical model is very complex. Due to these challenges, using model-based control approaches, such as model predictive control, often becomes infeasible. To this end, an adaptive two-degree of freedom control concept for reference tracking tasks is proposed. A data-driven surrogate model of the coupled system is trained, focusing on the reference tracking objective, and exploiting the model structure. This surrogate model is used in a two-degree of freedom control concept, splitting the tasks into reference tracking and disturbance rejection. The proposed control concept applies to a broad class of reference tracking processes subject to partially unknown system dynamics. It is tested exemplarily to control an interconnected three-tank system.