Iterative Learning Control and Gaussian Process Regression for Hydraulic Cushion Control

In this paper, we investigate on extending a feed-forward control scheme for the force control circuit of a hydraulic cushion with Gaussian Process nonlinear regression and Iterative Learning Control. Gaussian Processes allow the possibility of estimating the unknown proportional valve nonlinearities and provide uncertainty measurements of the predictions. However, the system must realize a high precision tracking control which is not achievable if any uncertainty remains in the estimation. Therefore, an extra feed-forward signal based on Iterative Learning Control is used to obtain a precise and fast force reference tracking performance. The design of the Iterative Learning Control is based on an inverted linearized model in which a fourth-order low-pass filter is included to attenuate the unknown valve dynamics. The low-pass filter is split up into two second-order low-pass filters, one of which is applied in the positive, the other in the negative, direction of time, resulting in zero-phase filtering. Simulation results show that Gaussian Process regression allows the possibility of using feed-forward control and that the force tracking performance is improved by introducing Iterative Learning Control.This work has been partially funded by the Department of Development and Infrastructures of the Government of the Basque Country, via Industrial Doctoral Program BIKAINTEK (Official Bulleting of the Basque Country no 67 on 09/04/1

Iterative learning control in the commissioning of industrial presses

182 p.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

Hydraulic Press Commissioning Cost Reductions via Machine Learning Solutions

Abstract publicado en EUROSIM 2019 Abstract Volume. ARGESIM Report 58, ISBN: 978-3-901608-92-6 (ebook), DOI: 10.11128/arep.58In industrial processes, PI controllers remain as the dominant control technique due to their applicability and performance reliability. However, there could be applications where the PI controller is not enough to fulfill certain specifications, such as in the force control loop of hydraulic presses, in which specific pressure profiles need to be ensured in order not to damage theworkpiece. An Iterative Learning Control scheme is presented as a Machine Learning control alternative to the PI controller, in order to track the pressure profiles required for any operational case. Iterative Learning Control is based on the notion that a system that realizes the same process repeatedly, e.g. hydraulic presses, can improve its performance by learning from previous iterations. The improvements are revealed in high-fidelity simulations of a hydraulic press model, in which the tracking performance of the PI controller is considerably improved in terms of overshoot and the settling time of pressure signal.UPV/EHU, Grupo de Investigación de Inteligencia Computaciona

Hydraulic Press Commissioning Cost Reductions via Machine Learning Solutions

Abstract publicado en EUROSIM 2019 Abstract Volume. ARGESIM Report 58, ISBN: 978-3-901608-92-6 (ebook), DOI: 10.11128/arep.58In industrial processes, PI controllers remain as the dominant control technique due to their applicability and performance reliability. However, there could be applications where the PI controller is not enough to fulfill certain specifications, such as in the force control loop of hydraulic presses, in which specific pressure profiles need to be ensured in order not to damage theworkpiece. An Iterative Learning Control scheme is presented as a Machine Learning control alternative to the PI controller, in order to track the pressure profiles required for any operational case. Iterative Learning Control is based on the notion that a system that realizes the same process repeatedly, e.g. hydraulic presses, can improve its performance by learning from previous iterations. The improvements are revealed in high-fidelity simulations of a hydraulic press model, in which the tracking performance of the PI controller is considerably improved in terms of overshoot and the settling time of pressure signal.UPV/EHU, Grupo de Investigación de Inteligencia Computaciona

Volume 3 – Conference: Thursday, March 10

10. Internationales Fluidtechnisches Kolloquiu

Volume 2 – Conference: Wednesday, March 9

10. Internationales Fluidtechnisches Kolloquium:Group 1 | 2: Novel System Structures Group 3 | 5: Pumps Group 4: Thermal Behaviour Group 6: Industrial Hydraulic

Aeronautical Engineering: A special bibliography with indexes, supplement 37

This special bibliography lists 511 reports, articles, and other documents introduced into the NASA scientific and technical information system in October, 1973

Foot/Ankle Prostheses Design Approach Based on Scientometric and Patentometric Analyses

There are different alternatives when selecting removable prostheses for below the knee amputated patients. The designs of these prostheses vary according to their different functions. These prostheses designs can be classified into Energy Storing and Return (ESAR), Controlled Energy Storing and Return (CESR), active, and hybrid. This paper aims to identify the state of the art related to the design of these prostheses of which ESAR prostheses are grouped into five types, and active and CESR are categorized into four groups. Regarding patent analysis, 324 were analyzed over the last six years. For scientific communications, a bibliometric analysis was performed using 104 scientific reports from the Web of Science in the same period. The results show a tendency of ESAR prostheses designs for patents (68%) and active prostheses designs for scientific documentation (40%).Beca Conacyt Doctorad

Whole Body Vibrations during Fully Mechanised Logging

This paper seeks to answer the question of whether the magnitude of vibrations affecting the whole body of the harvester operator (WBV) that are generated by the harvester boom is affected by the size of the processed trunk volume, to specify closer, the magnitude of WBVs generated during forest logging, and to localise these WBVs in individual partial operations. For these purposes, the production process, i.e., forest logging, was divided into six partial operations (Searching; Felling; Processing; Unproductive time; Machine movement; Stationary position). WBVs were scanned in the respective partial operations according to standard ISO 2631-1:1997 and the European Directive 2002/44/EC, and then the values were mutually compared. Volumes of processed trunks were recorded, which were then assigned to the given WBV during the respective operations. Research results did not demonstrate a correlation between the size of the transmitted vibrations and the volumes of cut trunks in the partial work operations of Felling and Processing. Neither a difference was found between the individual partial operations with two exceptions: Searching and Felling/Processing and Unproductive time. The research further showed that the average WBV of three partial operations did not meet the daily limit of 0.50 m/s2 permitted by European Directive 2002/44/EC, within a range from 12.20% to 27.02%.O