Predictive adaptive temperature control in a solar furnace for material stress tests

This paper explores the adaptive predictive Musmar algorithm to control the temperature of material samples in a solar furnace. Solar furnaces are devices that are powered by concentrated solar energy and are used in material stress applications. Solar furnaces are characterized by having a nonlinear dynamics, the nonlinear effect between the incident energy and the temperature of the sample, the nonlinear relation between the positioning of the shutter and the incident power applied on the sample, the interaction between the shutter dynamics and the temperature dynamics of the sample and the presence of fast perturbations on sunlight induced by clouds. This work is being developed in the framework of the SFERA project, to automate material stress experiments in solar furnaces of the Odeillo Processes Materials and Solar Energy Laboratory, located at the Oriental Pyrenees in the South of France.