CUBES Circle represents, from control engineering perspective, a highly complex production system whose individual elements must be coordinated to realize closed energy and mass flows. In order to achieve this goal, SP5 introduces modeling methods as well as model-based robust, adaptive and optimal control methods to the project.
Modeling Methods: Mathematical formulation of dynamic models of the energy and mass flows of the CUBES-Circle will be used. These models will be parameterized and validated by means of parameter identification and machine learning/AI based on experimentally collected data from the production CUBES (SP2, SP3, SP4) and results from SP7.
Control Methods: Formulation of the quality criteria of the control techniques and the design of optimal control for energy and mass flow between the cubes will be based on the derived and analyzed models. It is important to synergistically compensate for the fluctuations that occur during operation (e.g. variations in cultivation, weather, variations in growth conditions) and to ensure stable and safe circulation. Based on the collected data from the production CUBES, the aim is to improve the models and control using Big Data analysis and learning methods. A combination of optimal, learning and adaptive control methods shall be considered to increase the efficiency as well as flexibility and adaptability of the CUBES Circle to new production sites and conditions.
Prof. Dr.-Ing. habil Stefan Streif (ControlSystem)
PhD, Dr.-Ing. Arne-Jens Hempel