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Wallén, Johanna
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Wallén, J., Norrlöf, M. & Gunnarsson, S. (2011). A Framework for Analysis of Observer-Based ILC. Asian journal of control, 13(1), 3-14
Open this publication in new window or tab >>A Framework for Analysis of Observer-Based ILC
2011 (English)In: Asian journal of control, ISSN 1561-8625, E-ISSN 1561-8625, Vol. 13, no 1, p. 3-14Article in journal (Refereed) Published
Abstract [en]

A framework for iterative learning control (ILC) is proposed for the situation when an ILC algorithm uses an estimate of the controlled variable obtained from an observer-based estimation procedure. Assuming that the ILC input converges to a bounded signal, a general expression for the asymptotic error of the controlled variable is given. The asymptotic error is exemplified by an ILC algorithm applied to a flexible two-mass model of a robot joint.

Place, publisher, year, edition, pages
John Wiley & Sons, 2011
Keywords
Iterative learning control, Framework, Estimate, Asymptotic, Controlled variable
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-67570 (URN)10.1002/asjc.261 (DOI)000286491600002 ()
Available from: 2011-04-18 Created: 2011-04-18 Last updated: 2017-12-11
Wallén, J. (2011). Estimation-based iterative learning control. (Doctoral dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Estimation-based iterative learning control
2011 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

In many  applications industrial robots perform the same motion  repeatedly. One way of compensating the repetitive part of the error  is by using iterative learning control (ILC). The ILC algorithm  makes use of the measured errors and iteratively calculates a  correction signal that is applied to the system.

The main topic of the thesis is to apply an ILC algorithm to a  dynamic system where the controlled variable is not measured. A  remedy for handling this difficulty is to use additional sensors in  combination with signal processing algorithms to obtain estimates of  the controlled variable. A framework for analysis of ILC algorithms  is proposed for the situation when an ILC algorithm uses an estimate  of the controlled variable. This is a relevant research problem in  for example industrial robot applications, where normally only the  motor angular positions are measured while the control objective is  to follow a desired tool path. Additionally, the dynamic model of  the flexible robot structure suffers from uncertainties. The  behaviour when a system having these difficulties is controlled by  an ILC algorithm using measured variables directly is illustrated  experimentally, on both a serial and a parallel robot, and in  simulations of a flexible two-mass model. It is shown that the  correction of the tool-position error is limited by the accuracy of  the robot model.

The benefits of estimation-based ILC is illustrated for cases when  fusing measurements of the robot motor angular positions with  measurements from an additional accelerometer mounted on the robot  tool to form a tool-position estimate. Estimation-based ILC is  studied in simulations on a flexible two-mass model and on a  flexible nonlinear two-link robot model, as well as in experiments  on a parallel robot. The results show that it is possible to improve  the tool performance when a tool-position estimate is used in the  ILC algorithm, compared to when the original measurements available  are used directly in the algorithm. Furthermore, the resulting  performance relies on the quality of the estimate, as expected.

In the last part of the thesis, some implementation aspects of ILC  are discussed. Since the ILC algorithm involves filtering of signals  over finite-time intervals, often using non-causal filters, it is  important that the boundary effects of the filtering operations are  appropriately handled when implementing the algorithm. It is  illustrated by theoretical analysis and in simulations that the  method of implementation can have large influence over stability and  convergence properties of the algorithm.

Abstract [sv]

Denna avhandling behandlar reglering genom iterativ inlärning, ILC  (från engelskans iterative learning control). Metoden har sitt  ursprung i industrirobottillämpningar där en robot utför samma  rörelse om och om igen. Ett sätt att kompensera för felen är genom  en ILC-algoritm som beräknar en korrektionssignal, som läggs på  systemet i nästa iteration. ILC-algoritmen kan ses som ett  komplement till det befintliga styrsystemet för att förbättra  prestanda.

Det problem som särskilt studeras är då en ILC-algoritm appliceras  på ett dynamiskt system där reglerstorheten inte mäts. Ett sätt att  hantera dessa svårigheter är att använda ytterligare sensorer i  kombination med signalbehandlingsalgoritmer för att beräkna en  skattning av reglerstorheten som kan användas i ILC-algoritmen. Ett  ramverk för analys av skattningsbaserad ILC föreslås i avhandlingen.  Problemet är relevant och motiveras utifrån experiment på både en  seriell och en parallel robot. I konventionella robotstyrsystem  mäts endast de enskilda motorpositionerna, medan verktygspositionen  ska följa en önskad bana. Experimentresultat visar att en  ILC-algoritm baserad på motorpositionsfelen kan reducera dessa fel  effektivt. Dock behöver detta inte betyda en förbättrad  verktygsposition, eftersom robotmotorerna styrs mot felaktiga värden  på grund av att modellerna som används för att beräkna dessa  referensbanor inte beskriver den verkliga robotdynamiken helt.

Skattningsbaserad ILC studeras både i simulering av en flexibel  tvåmassemodell och en olinjär robotmodell med flexibla leder, och i  experiment på en parallell robot. I studierna sammanvägs  motorpositionsmätningar med mätningar från en accelerometer på  robotverktyget till en skattning av verktygspositionen som används i  ILC-algoritmen. Resultaten visar att det är möjligt att förbättra  verktygspositionen med skattningsbaserad ILC, jämfört med när  motorpositionsmätningarna används direkt i  ILC-algoritmen. Resultatet beror också på skattningskvaliteten, som  förväntat.

Slutligen diskuteras några implementeringsaspekter. Alla värden i  uppdateringssignalen läggs på systemet samtidigt, vilket gör det  möjligt att använda icke-kausal filtering där man utnyttjar framtida  signalvärden i filteringen. Detta gör att det är viktigt hur  randeffekterna (början och slutet av signalen) hanteras när man  implementerar ILC-algoritmen. Genom teoretisk analys och  simuleringsexempel illustreras att implementeringsmetoden kan ha  stor betydelse för egenskaperna hos ILC-algoritmen.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. p. 180
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1358
Keywords
Iterative learning control, estimation, industrial robotics, performance
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-64017 (URN)978-91-7393-255-4 (ISBN)
Public defence
2011-02-11, Visionen, Hus B, Campus Valla, Linköping University, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2011-01-13 Created: 2011-01-11 Last updated: 2019-12-19Bibliographically approved
Wallén, J., Norrlöf, M. & Gunnarsson, S. (2009). A Framework for Analysis of Observer-Based ILC. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>A Framework for Analysis of Observer-Based ILC
2009 (English)Report (Other academic)
Abstract [en]

A framework for Iterative Learning Control (ILC) is proposed for the situation when the ILC algorithm is based on an estimate of the controlled variable obtained from an observer-based estimation procedure. Under the assumption that the ILC input converges to a bounded signal, a general expression for the asymptotic error of the controlled variable is given. The asymptotic error is then exemplified by an ILC algorithm applied to a flexible two-mass model of a robot joint.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2009. p. 11
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2918
Keywords
Iterative learning control, Framework, Estimate, Asymptotic, Controlled variable
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-56210 (URN)LiTH-ISY-R-2918 (ISRN)
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-10-01Bibliographically approved
Wallén, J., Gunnarsson, S., Henriksson, R., Moberg, S. & Norrlöf, M. (2009). ILC Applied to a Flexible Two-Link Robot Model using Sensor-Fusion-Based Estimates. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>ILC Applied to a Flexible Two-Link Robot Model using Sensor-Fusion-Based Estimates
Show others...
2009 (English)Report (Other academic)
Abstract [en]

Estimates from an extended Kalman filter (EKF) is used in an Iterative Learning Control (ILC) algorithm applied to a realistic two-link robot model with flexible joints. The angles seen from the arm side of the joints (arm angles) are estimated by an EKF in two ways: 1) using  measurements of angles seen from the motor side of the joints (motor angles), which normally  are the only measurements available in commercial industrial robot systems, 2) using both motor- angle and tool-acceleration measurements. The estimates are then used in an ILC algorithm. The results show that the actual arm angles are clearly improved compared to when only motor angles are used in the ILC update, even though model errors are introduced.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2009. p. 9
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2880
Keywords
Iterative learning control; Robotics; Sensor fusion
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-56194 (URN)LiTH-ISY-R-2880 (ISRN)
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-10-01Bibliographically approved
Wallén, J., Norrlöf, M. & Gunnarsson, S. (2008). Arm-Side Evaluation of ILC Applied to a Six-Degrees-of-Freedom Industrial Robot. In: Proceedings of the 17th IFAC World Congress. Paper presented at 17th IFAC World Congress, Seoul, South Korea, July, 2008 (pp. 13450-13455).
Open this publication in new window or tab >>Arm-Side Evaluation of ILC Applied to a Six-Degrees-of-Freedom Industrial Robot
2008 (English)In: Proceedings of the 17th IFAC World Congress, 2008, , p. 13450-13455p. 13450-13455Conference paper, Published paper (Refereed)
Abstract [en]

Experimental results from a first-order ILC algorithm applied to a large-size sixdegrees-of-freedom commercial industrial robot are presented. The ILC algorithm is based on measurements of the motor angles, but in addition to the conventional evaluation of the ILC algorithm based on the motor-side error, the tool-path error on the arm side is evaluated using a laser-measurement system. Experiments have been carried out in three operating points using movements that represent typical paths in a laser-cutting application and different choices of algorithm design parameters have been studied. The motor-angle error is reduced substantially in all experiments and the tool-path error is reduced in most of the cases. In one operating point, however, the error does not decrease as much and an oscillatory tool behaviour is observed. Changed filter variables can give worse error reduction in all operating points. To achieve even better performance, especially in difficult operating points, it is concluded that an arm-side measurement, from for example an accelerometer, needs to be included in the learning.

Publisher
p. 13450-13455
Keywords
Iterative methods, Learning control, Control applications, Industrial robots, Position control
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-43202 (URN)10.3182/20080706-5-KR-1001.02278 (DOI)72900 (Local ID)978-3-902661-00-5 (ISBN)72900 (Archive number)72900 (OAI)
Conference
17th IFAC World Congress, Seoul, South Korea, July, 2008
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-02-23
Wallén, J., Norrlöf, M. & Gunnarsson, S. (2008). Comparison of Performance and Robustness for two Classical ILC Algorithms Applied to a Flexible System. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Comparison of Performance and Robustness for two Classical ILC Algorithms Applied to a Flexible System
2008 (English)Report (Other academic)
Abstract [en]

When an ILC algorithm is applied to an industrial robot, the goal is to move the tool along a desired trajectory, while only the motor position canbe measured. In this paper aspects of robustness and performance are discussed when an ILC algorithm is applied to a flexible two-mass system. It is shown that the stabilising controller of the two-mass system also directly affects the robustness properties of the ILC algorithm. A classical noncausa lP-ILC algorithm and a model-based ILC design using optimisation are applied to the system, based on the error for the first mass. Performance and robustness of the algorithms are compared when model errors are introduced in the system, showing that the optimisation-based approach can handle larger model uncertainties. It is illustrated that the performance of the overall system, when considering position of the second mass, is the practical limit compared to the limiting factor of the robustness of the ILC algorithms.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2008. p. 10
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2868
Keywords
Iterative learning control, Robotics, Robust control
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-56181 (URN)LiTH-ISY-R-2868 (ISRN)
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-10-01Bibliographically approved
Wallén, J. (2008). On Kinematic Modelling and Iterative Learning Control of Industrial Robots. (Licentiate dissertation). : Institutionen för systemteknik
Open this publication in new window or tab >>On Kinematic Modelling and Iterative Learning Control of Industrial Robots
2008 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

Good models of industrial robots are necessary in a variety of applications, such as mechanical design, performance simulation, control, diagnosis, supervision and offline programming. This motivates the need for good modelling tools. In the first part of this thesis the forward kinematic modelling of serial industrial robots is studied. The first steps towards a toolbox are implemented in the Maple programming language.

A series of possible applications for the toolbox can be mentioned. One example is to estimate the pose of the robot tool using an extended Kalman filter by means of extra sensors mounted on the robot. The kinematic equations and the relations necessary for the extended Kalman filter can be derived in the modelling tool. Iterative learning control, ILC, using an estimate of the tool position can then improve the robot performance.

The second part of the thesis is devoted to ILC, which is a control method that is applicable when the robot performs a repetitive movement starting from the same initial conditions every repetition. The algorithm compensates for repetitive errors by adding a correction signal to the reference. Studies where ILC is applied to a real industrial platform is less common in the literature, which motivates the work in this thesis.

A first-order ILC filter with iteration-independent operators derived using a heuristic design approach is used, which results in a non-causal algorithm. A simulation study is made, where a flexible two-mass model is used as a simplified linear model of a single robot joint and the ILC algorithm applied is based on motor-angle measurements only. It is shown that when a model error is introduced in the relation between the arm and motor reference angle, it is not necessary that the error on the arm side is reduced as much as the error on the motor side, or in fact reduced at all.

In the experiments the ILC algorithm is applied to a large-size commercial industrial robot, performing a circular motion that is relevant for a laser-cutting application. The same ILC design variables are used for all six motors and the learning is stopped after five iterations, which is motivated in practice by experimental results. Performance on the motor side and the corresponding performance on the arm side, using a laser-measurement system, is studied. Even though the result on the motor side is good, it is no guarantee that the errors on the arm side are decreasing. One has to be very careful when dealing with resonant systems when the controlled variable is not directly measured and included in the algorithm. This indicates that the results on the arm side may be improved when an estimate of, for example, the tool position is used in the ILC algorithm.

Abstract [sv]

Bra modeller av industrirobotar behövs i en mängd olika tillämpningar, som till exempel mekanisk design, simulering av prestanda, reglering, diagnos, övervakning och offline-programmering. I första delen av avhandlingen studeras modellering av framåtkinematiken för en seriell robot och implementeringen av ett modelleringsverktyg, en toolbox, för kinematikmodellering i Maple beskrivs ingående.

Ett antal möjliga tillämpningar för toolboxen kan nämnas. Ett exempel är att med hjälp av extra sensorer monterade på roboten och ett så kallat extended Kalmanfilter förbättra skattningen av positionen och orienteringen för robotverktyget. De kinematiska ekvationerna och sambanden som behövs för extended Kalmanfiltret kan beräknas med hjälp av modelleringsverktyget. Reglering genom iterativ inlärning - iterative learning control, ILC - där en skattning av verktygspositionen används, kan sedan förbättra robotens prestanda.

Andra delen av avhandlingen är tillägnad ILC. Det är en reglermetod som är användbar när roboten utför en repetitiv rörelse som startar från samma initialvillkor varje gång. Algoritmen kompenserar för de repetitiva felen genom att addera en korrektionsterm till referenssignalen. Studier där ILC är tillämpad på en verklig industriell plattform är mindre vanligt i litteraturen, vilket motiverar arbetet i avhandlingen.

Ett första ordningens ILC-filter med iterationsoberoende operatorer används. ILC-algoritmen är framtagen enligt ett heuristiskt tillvägagångssätt, vilket resulterar i en ickekausal algoritm. I en simuleringsstudie med en flexibel tvåmassemodell som en förenklad linjär modell av en enskild robotled, används en ILC-algoritm baserad endast på motorvinkelmätningar. Det visar sig att när ett modellfel introduceras i sambandet mellan arm- och motorvinkelreferensen, är det inte säkert att felet på armsidan minskar så mycket som felet på motorsidan, eller minskar överhuvudtaget.

I experiment tillämpas ILC-algoritmen på en stor kommersiell industrirobot som utför en cirkelrörelse som är relevant för en laserskärningstillämpning. Samma designvariabler används för alla sex motorerna och inlärningen stoppas efter fem iterationer, vilket är motiverat i praktiken genom experimentella resultat. Prestanda på motorsidan studeras, och motsvarande prestanda på armsidan mäts med ett lasermätsystem. Trots goda resultat på motorsidan finns det inga garantier för minskande fel på armsidan. Stor försiktighet krävs när experimenten innefattar ett resonant system där den reglerade variabeln inte är mätt explicit och inkluderad i algoritmen. Detta visar på möjligheten att förbättra resultaten på armsidan då en skattning av till exempel verktygspositionen används i ILC-algoritmen.

Place, publisher, year, edition, pages
Institutionen för systemteknik, 2008. p. 127
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1343
Keywords
Industrial robots, kinematics, Maple, modelling, iterative learning control, experiments
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-11412 (URN)978-91-85715-00-8 (ISBN)
Presentation
2008-01-25, Planck, Fysikhuset, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Note
Report code: LiU-TEK-LIC-2008:1.Available from: 2008-04-04 Created: 2008-04-04 Last updated: 2009-03-06
Wallén, J., Norrlöf, M. & Gunnarsson, S. (2008). Performance and Robustness of ILC Applied to Flexible Systems. In: Proceedings of Reglermöte 2008. Paper presented at Reglermöte 2008, Luleå, Sweden, June, 2008 (pp. 210-216).
Open this publication in new window or tab >>Performance and Robustness of ILC Applied to Flexible Systems
2008 (English)In: Proceedings of Reglermöte 2008, 2008, p. 210-216Conference paper, Published paper (Other academic)
Abstract [en]

When an ILC algorithm is applied to an industrial robot, the goal is to move the tool along a desired trajectory, while only the motor position can be measured. In this paper aspects of robustness and performance are discussed when an ILC algorithm is applied to a flexible two-mass system. It is shown that the stabilising controller of the two-mass system also directly affects the robustness properties of the ILC algorithm. A classical non-causal P-ILC algorithm and a model-based ILC design using optimisation are applied to the system, based on the error for the first mass. Performance and robustness of the algorithms are compared when model errors are introduced in the system, showing that the optimisation-based approach can handle larger model uncertainties. It is illustrated that the performance of the overall system, when considering position of the second mass, is the practical limit compared to the limiting factor of the robustness of the ILC algorithms.

Keywords
Iterative learning control, Robustness, Performance, Industrial robots, Simulation
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-43201 (URN)72899 (Local ID)72899 (Archive number)72899 (OAI)
Conference
Reglermöte 2008, Luleå, Sweden, June, 2008
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-02-23
Wallén, J., Norrlöf, M. & Gunnarsson, S. (2008). Performance of ILC Applied to a Flexible Mechanical System. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Performance of ILC Applied to a Flexible Mechanical System
2008 (English)Report (Other academic)
Abstract [en]

ILC is traditionally applied to systems where the controlled variable is the measured variable. However, in standard industrial robots only the motor angles are measured, while the control objective is to follow a tool path. A modern industrial robot is flexible, and assuming that the mechanical flexibilities are concentrated to the robot joints (elastic gearboxes), a flexible two-mass model can be used to describe a single joint. A P-ILC algorithm is applied to the two-mass model, based on only measured angle of the first mass (motor angle) or estimated angle for the second mass (tool angle). Robustness of the algorithm, and performance when model errors are introduced in the model, are discussed considering the error of the tool angle. First, it can be concluded for a flexible system that the characteristics of the motor-angle reference is essential for the resulting tool angle when the tool angle cannot be measured. Second, using an estimate of the tool angle instead of the explicit motor angle in the ILC update reduces the tool-angle error significantly.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2008. p. 9
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2869
Keywords
Iterative learning control, Robotics, Robust control
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-56180 (URN)LiTH-ISY-R-2869 (ISRN)
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-10-01Bibliographically approved
Wallén, J. (2008). The History of the Industrial Robot. Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>The History of the Industrial Robot
2008 (English)Report (Other academic)
Abstract [en]

In this report some phenomena and events in the history of industrial robots have been described. The prerequisites are mainly the early automation in the industry, together with the playful automatons. With the computer and later on the integrated circuit, it was possible to develop the first industrial robots. The first robots were used for simple tasks as pick and place, since they had no external sensing. They replaced humans in monotonous, repetitive, heavy and dangerous tasks. When the robots could manage both a more complex motion, but also had external sensor capacity, more complex applications followed, like welding, grinding, deburring and assembly. The usage of industrial robots can nowadays,roughly speaking, be divided into three different groups; materialhandling, process operations and assembly. In general, industrial robots are used to reduce costs, increase productivity, improve product quality and eliminate harmful tasks. These areas represent the main factors resulting in the spread of robotics technology in a wider and wider range of applications in manufacturing industry. However, introducing robots do not solve all problems. Automation, productivity, employment are complex questions and the connections between robots and labour can be discussed much more.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2008. p. 18
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2853
Keywords
Robot, Automation, History
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-56167 (URN)LiTH-ISY-R-2853 (ISRN)
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-10-02
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