Iterative learning control in the commissioning of industrial presses

  1. Trojaola Bolinaga, Ignacio
Supervised by:
  1. Iker Elorza Pinedo Director
  2. Eloy Irigoyen Gordo Director

Defence university: Universidad del País Vasco - Euskal Herriko Unibertsitatea

Fecha de defensa: 17 December 2021

  1. J. María Rossell Garriga Chair
  2. Isabel Sarachaga González Secretary
  3. Luka Eciolaza Echeverría Committee member
  1. Ingeniería de Sistemas y Automática

Type: Thesis

Teseo: 156734 DIALNET lock_openADDI editor


This thesis presents solutions to the control problems that exist nowadays in industrial presses, followed by a discussion of the most appropriate control schemes that may be used for their solution. Iterative Learning Control is subsequently analyzed, as the most promising control scheme for machine presses, due to its capability to improve the performance of a system that operates repeatedly.A novel Iterative Learning Control design is presented, which makes use of the dynamic characteristics of the system to improve the current controller performance and stability. This, results in an adaptation of the presented Iterative Learning Control design to two use cases: the single-input-single-output force control of mechanical presses and the multiple-input-multiple-output position control of hydraulic presses. While existing Iterative Learning Control approaches are also described and applied to the previously mentioned use cases, the presented novel approach has been shown to outperform the existing algorithms in terms of control performance.The proposed Iterative Learning control algorithms are validated in an experimental hydraulic test rig, in which the performance, robustness and stability of the algorithm have been demonstrated.