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Iterative Learning Control is the most suitable control technique for industrial presses due to its ability to improve the performance of a system that operates repeatedly. This is the main conclusion reached by a research in which Fagor Arrasate has participated.
Under the title “Iterative learning control in the commissioning of industrial presses”, Ignacio Trojaola presented his doctoral thesis at the University of the Basque Country/Euskal Herriko Unibertsitatea (UPV/ EHU) in which he analyzes in depth the different solutions to the problems of force control that exist today in both presses and hydraulic cushions.
In the thesis, of which the Innovation Director of Fagor Arrasate, Andoitz Aranburu has acted as co-tutor, a new Iterative Learning Control design has been developed that makes use of the dynamic characteristics of the system to improve the performance and stability of the controller. Through a continuous learning and correction process, it allows to better control the force peaks acting on different elements of the system. Thus, better control of the process is achieved, which, in turn, results in better part quality, protection and tool and fixture life.
The Iterative Learning Control algorithms developed have been validated in tests against digital twins of industrial presses and in a hydraulic test bench, in which it has been demonstrated the robustness and stability of the developed algorithms and that they offer a superior performance to the existing algorithms in terms of control performance.
The thesis has been awarded the IDOM Prize for the best Doctoral Thesis in Intelligent Control. This recognition is awarded by the Intelligent Control Thematic Group of the Spanish Committee of Automatics (CEA) during the XLIII Jornadas AUTOMÁTICA 2022, organized by the Spanish Committee of Automatics and held at the University of La Rioja, in Logroño (Spain)