Flexible Physical Process Control Through Predictor-Corrector Differential Models in Industry 4.0 Scenarios

Abstract

Industry 4.0 refers a new era in the human societies characterized by a high efficiency, specialization and the use of innovative technological solutions such as Cyber-Physical Systems. Although high-level services are envisioned to be the main economic activity in the fourth industrial revolution, traditional industrial manufacturing processes will be still essential and strategic. However, in Industry 4.0 scenarios, these processes must be highly efficient, reducing in a relevant way the resource consumption and waste generation. In order to do that, very precise control solutions for physical processes are needed, so the process can be guided across the optimal physical path. Nevertheless, current control solutions cannot perform these actions, as they are not dynamic or flexible enough to adapt to the random evolution of natural phenomena quickly and precisely. Thus, in this paper it is proposed a new and more flexible control mechanism for physical processes in Industry 4.0 scenarios. The proposed solution models physical processes as differential systems where Taylor series are employed to represent the unknown interdependency of physical variables. Besides, variable parameters and adaptative control functions are introduced to make the differential model follow the real process evolution. Using numerical methods, the whole differential model may be solved to predict future states, which are later corrected considering the real process evolution. Finally, in order to evaluate the performance of the proposed technology, an experimental validation based on simulation tools is provided.