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Experimental evaluation of a method for improving experiment design in robot identification
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-6523-8499
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
ABB Robotics, Västerås, Sweden.
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2023 (English)In: 2023 IEEE International Conference on Robotics and Automation (ICRA) / [ed] Marcia K. O'Malley, IEEE , 2023, p. 11432-11438Conference paper, Published paper (Refereed)
Abstract [en]

The control system of industrial robots is often model-based, and the quality of the model of high importance. Therefore, a fast and easy-to-use process for finding the model parameters from a combination of prior knowledge and measurement data is required. It has been shown that the experiment design can be improved in terms of short experiment times and an accurate parameter estimate if the robot configurations for the identification experiments are selected carefully. Estimates of the information matrix can be generated based on simulations for a number of candidate configurations, and an optimization problem can be solved for finding the optimal configurations. This work shows that the proposed method for improved experiment design works with a real manipulator, i.e. it is demonstrated that the experiment time is reduced significantly and the accuracy of the parameter estimate can be maintained or reduced if experiments are conducted only in the optimal manipulator configurations. It is also shown that the model improvement is relevant for realizing accurate control. Finally, the experimental data reveals that, in order to further improve the model accuracy, a more advanced model structure is needed for taking into account the commonly present nonlinear transmission stiffness of the robotic joints.

Place, publisher, year, edition, pages
IEEE , 2023. p. 11432-11438
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-196489DOI: 10.1109/icra48891.2023.10161092ISI: 001048371103078ISBN: 9798350323658 (print)ISBN: 9798350323665 (electronic)OAI: oai:DiVA.org:liu-196489DiVA, id: diva2:1786463
Conference
IEEE International Conference on Robotics and Automation (ICRA), 29th May - 2nd June 2023, ExCel London
Note

Funding: Vinnova competence center LINK-SIC

Available from: 2023-08-09 Created: 2023-08-09 Last updated: 2023-10-11
In thesis
1. Data-driven Modeling of Robotic Manipulators – Efficiency Aspects
Open this publication in new window or tab >>Data-driven Modeling of Robotic Manipulators – Efficiency Aspects
2023 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Robotic manipulators are used for industrial automation and play an important role in manufacturing industry. Increasing performance requirements such as high operating speed and motion accuracy conflict with demands on heavy pay-loads and light-weight design with reduced structural stiffness. The motion control system is a key factor for dealing with these requirements, particularly for increasing the robot performance, improving safety and reducing power consumption. Most industrial robot control systems rely on current and angular position measurements from the motors, meaning that the actual controlled variable, that is the position of the robot’s end-effector, needs to be calculated using a model. Therefore, the mathematical model used for motion control must accurately describe the system’s dynamic behavior. Based on physics equations, the model contains unknown parameters that are usually identified from experimental data. This identification is a challenging problem, since the equations are nonlinear in the parameters, the system is highly resonant and experiments can only be done in closed loop with a controller. 

Assuming a real robot is available for experiments, data-driven identification is common in order to obtain the most accurate description of the real system’s behavior. The method applied in this thesis estimates the dynamic stiffness parameters by matching the model’s frequency response function to the system’s frequency response, which is obtained from measurements done with the closed-loop robot system. The main focus of this thesis are strategies for increasing the process efficiency such that the time it takes to do the experiments is reduced, while the quality of the model is maintained or improved. Two strategies related to experiment design are presented: First, the number of quasi-static robot configurations for data collection is decreased by choosing the most informative configurations from a set of candidates. Second, less data-demanding methods for estimating the system’s frequency response are considered. The effectiveness of the presented approaches is demonstrated both in simulation and with real data. 

If no robot is available for experiments, e.g. in the development phase, a model must be built based on specification data of components and other information available to the designer, such as CAD data. This thesis contains a modeling approach that derives a high-fidelity robot model of low order (lumped parameter model with few degrees of freedom) by combining results from test-rig measurements of isolated components with carefully reduced finite element models of the robot’s structural parts. 

Abstract [sv]

Robotmanipulatorer används för industriell automation och de spelar en viktig roll inom tillverkningsindustrin. Ökande prestandakrav som hög hastighet och noggrannhet hos robotens rörelse står i konflikt med trenden att bygga lättviktsrobotar som kan hantera tunga laster och som samtidigt är säkra för att jobba nära människor. Robotens styrsystem är en nyckelfaktor för att hantera dessa krav, särskilt för att öka robotens prestanda, förbättra säkerheten och minska strömförbrukningen. I de flesta tillämpningar är styrsystemets uppgift att säkerställa att robotens hand gör den önskade rörelsen, d.v.s. att handens position och hastighet motsvarar användarprogrammet. Positionen och hastigheten hos robotens hand är inte mätbara med sensorerna som är inbyggda i vanliga industriella robotar, vilket gör att de måste beräknas med hjälp av en matematisk modell. Denna modell måste beskriva det komplicerade sambandet mellan robotarmens rörelser och de motorer som orsakar rörelsen. Modellen är baserad på fysikaliska samband och innehåller okända parametrar som vanligtvis tas fram med hjälp av mätdata.

Det som mäts är position och moment hos robotens alla motorer och det som är eftersökt är parametrarna relaterat till robotens styvhet. Metoden som används i denna avhandling tar fram styvhetsparametrarna genom att matcha modellens frekvenssvarsfunktion med det uppmätta frekvenssvaret för den verkliga roboten. Huvudfokus är strategier för att öka processeektiviteten så att tiden det tar att utföra mätningarna minskar, samtidigt som modellens kvalitet bibehålls eller förbättras. Två strategier presenteras: Den första minskar antalet robotkonfigurationer för mätdatainsamling genom att välja de mest informativa konfigurationerna från ett antal kandidater. Den andra strategin bygger på mindre datakrävande metoder för att skatta robotens frekvenssvar. Effektiviteten av de presenterade strategierna visas både i simulering och med verklig mätdata.

Att få fram en bra matematisk modell är svårt om ingen robot är tillgänglig för mätningar, t.ex. i utvecklingsfasen av en ny robot. I så fall måste en modell byggas baserat på specifikationsdata för komponenter, t.ex. leverantörens information om växellådans styvhet, eller materialegenskaper för robotens struktur-delar. Styvheten av robotens strukturdelar kan beskrivas mycket noggrant med den så kallade finita element-metoden som delar strukturen i små delar och kombinerar ekvationerna för varje del till ett stort ekvationssystem. Detta ekvationssystem måste reduceras för att vara användbart i styrsystemets robotmodell. Denna avhandling innehåller ett modelleringssätt där man får fram en noggrann robotmodell genom att kombinera en reducerad styvhetsbeskrivning av robotens strukturdelar med specifikationsdata för komponenter.  

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2023. p. 63
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1963
National Category
Robotics
Identifiers
urn:nbn:se:liu:diva-193542 (URN)10.3384/9789180751988 (DOI)9789180751971 (ISBN)9789180751988 (ISBN)
Presentation
2023-05-22, Ada Lovelace, entrence 27, B-building, Campus Valla, Linköping, 10:15 (Swedish)
Opponent
Supervisors
Note

Funding: Vinnova competence center LINK-SIC.

2023-05-04: ISBN (PDF) has been added in the E-version.

Available from: 2023-05-04 Created: 2023-05-04 Last updated: 2023-08-09Bibliographically approved

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Zimmermann, Stefanie AntoniaEnqvist, MartinGunnarsson, Svante

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