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• 1.
Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology.
Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Divison of Neurobiology. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Division of Neuro and Inflammation Science. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Center for Social and Affective Neuroscience. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Automatic Control.
Design and Outcome of a CDIO Syllabus Survey for a Biomedicine Program2019In: The 15th International CDIO Conference: Proceedings – Full Papers, 2019, p. 191-200Conference paper (Refereed)

The CDIO Syllabus survey has successfully been applied to the Bachelor’s and Master’s programs in Experimental and Medical Biosciences, within the Faculty of Medicine and Health Sciences at Linköping University, Sweden. The programs are and have been, subject to considerable redesign with strong influence from the CDIO framework. One of the main drivers for the redesign is a shift concerning the main job market after graduation, from an academic career to industry and healthcare. One of the steps in the development process has been to carry out a Syllabus survey based on an adapted version of the CDIO Syllabus. The survey was sent out to students and to various categories of professionals, and in total 87 responses were received. The adapted version of the Syllabus and the design, execution, and outcome of the survey is presented.

• 2.
Univ Fed Minas Gerais, Brazil.
ABB AB, Sweden. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. ABB AB, Sweden. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Failure detection in robotic arms using statistical modeling, machine learning and hybrid gradient boosting2019In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 146, p. 425-436Article in journal (Refereed)

Modeling and failure prediction are important tasks in many engineering systems. For these tasks, the machine learning literature presents a large variety of models such as classification trees, random forest, artificial neural networks, among others. Standard statistical models such as the logistic regression, linear discriminant analysis, k-nearest neighbors, among others, can be applied. This work evaluates advantages and limitations of statistical and machine learning methods to predict failures in industrial robots. The work is based on data from more than five thousand robots in industrial use. Furthermore, a new approach combining standard statistical and machine learning models, named hybrid gradient boosting, is proposed. Results show that the hybrid gradient boosting achieves significant improvement as compared to statistical and machine learning methods. Furthermore, local joint information has been identified as the main driver for failure detection, whereas failure classification can be improved using additional information from different joints and hybrid models. (C) 2019 Elsevier Ltd. All rights reserved.

• 3.
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. ABB Robot, Sweden.
Some Controllability Aspects for Iterative Learning Control2019In: Asian journal of control, ISSN 1561-8625, E-ISSN 1561-8625, Vol. 21, no 3, p. 1057-1063Article in journal (Refereed)

Some controllability aspects for iterative learning control (ILC) are discussed. Via a batch (lifted) description of the problem a state space model of the system to be controlled is formulated in the iteration domain. This model provides insight and enables analysis of the conditions for and relationships between controllability, output controllability and target path controllability. In addition, the property miminum lead target path controllability is introduced. This property, which is connected to the number of time delays, plays an important role in the design of ILC algorithms. The properties are illustrated by a numerical example.

• 4.
Chalmers University of Technology, Gothenburg, Sweden.
Chalmers University of Technology, Gothenburg, Sweden. Chalmers University of Technology, Gothenburg, Sweden. KTH Royal Institute of Technology, Stockholm, Sweden. KTH Royal Institute of Technology, Stockholm, Sweden. DTU Technical University of Denmark, Lyngby, Denmark. DTU Technical University of Denmark, Lyngby, Denmark. DTU Technical University of Denmark, Lyngby, Denmark. Norwegian University of Science and Technology, Trondheim, Norway. Norwegian University of Science and Technology, Trondheim, Norway. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Automatic Control. Singapore Polytechnic, Singapore. TU Delft, Delft, The Netherlands.
Towards CDIO Standards 3.02019In: The 15th International CDIO Conference Proceedings – Full Papers / [ed] Jens Bennedsen, Aage Birkkjær Lauritsen, Kristina Edström, Natha Kuptasthien, Janne Roslöf & Robert Songer, 2019, p. 44-66Conference paper (Refereed)

The topic of this paper is the CDIO Standards, specifically the formulation of CDIO Standards version 3.0. The paper first reviews the potential change drivers that motivate a revision of the Standards. Such change drivers are identified both externally (i.e., from outside of the CDIO community) and internally. It is found that external change drivers have affected the perceptions of what problems engineers should address, what knowledge future engineers should possess and what are the most effective teaching practices in engineering education. Internally, the paper identifies criticism of the Standards, as well as ideas for development, that have been codified as proposed additional CDIO Standards. With references to these change drivers, five areas are identified for the revision: sustainability, digitalization of teaching and learning; service; and faculty competence. A revised version of the Standards is presented. In addition, it is proposed that a new category of Standards is established, “optional standards”. Optional Standards are a complement to the twelve “basic” Standards, and serve to guide educational development and profiling beyond the current Standards. A selected set of proposed optional Standards are recommended for further evaluation and possibly acceptance by the CDIO community

• 5.
Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Automatic Control.
Linköping University, Faculty of Science & Engineering. Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
Using Course and Program Matrices as Components in a Quality Assurance System2019In: The 15th International CDIO Conference: Proceedings – Full Papers, 2019, p. 110-119Conference paper (Refereed)

The CDIO framework is an integrated and important part of the new quality assurance system within the Faculty of Science and Engineering at Linköping University. Both the CDIO Syllabus and the CDIO Standards are used extensively in the system. First, the paper presents the development and use of the second generation of course matrices (previously denoted ITU-matrices) and program matrices, which build upon an adapted and extended version of the CDIO Syllabus. The extension is made to also include bachelor’s and master’s program in subjects outside the engineering field. Second, the paper presents how the CDIO Standards are used in the quality reports, which are vital parts of the quality assurance systems. As a result, the CDIO framework is used for the design, management, and quality assurance of all education programs ( approximately 60 programs) within the Faculty of Science and Engineering at Linköping University.

• 6.
Methodist University, Sao Paulo, Brasilien.
Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Automatic Control.
A conceive-design project in the first semester of the engineering programs at Methodist University of Sao Paulo2018Conference paper (Other academic)

A conceive-design project is presented. The project runs over the first two years of the engineering education programs at the Methodist University of São Paulo, and the paper focuses on the first semester of the Computer Engineering Program. The projects are carried out in teams of students from different programs and the results are presented to a board of faculty members and sometimes participants from our partner companies, at the end of each semester. There are around 200 students involved in total. For the first semester, students are required to conceive, design and document an environmentally-friendly product or service. The conceive-design project is integrated with surrounding modules is the curriculum, such as Entrepreneurship and Innovation and Economics, Society and Environmental Issues.

• 7.
AstaZero, Sweden.
ABB AB, Sweden. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Automatic Control. ABB AB, Sweden. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
A learning approach for feed-forward friction compensation2018In: Proceedings of the 12th IFAC Symposium on Robot Control, 2018Conference paper (Other academic)

An experimental comparison of two feed-forward based frictioncompensation methods is presented. The first method is based on theLuGre friction model, using identified friction model parameters, andthe second method is based on B-spline network, where the networkweights are learned from experiments. The methods are evaluated andcompared via experiments using a six axis industrial robot carryingout circular movements of different radii. The experiments show thatthe learning-based friction compensation gives an error reduction ofthe same magnitude as for the LuGre-based friction compensation.

• 8.
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. 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.
Comparing Feedback Linearization and Jacobian Linearization for LQ Control of an Industrial Manipulator2018In: Proccedings of the 12TH IFAC SYMPOSIUM ON ROBOT CONTROL, 2018Conference paper (Refereed)

Feedback linearization is compared to Jacobian linearization for LQ control of atwo-link industrial manipulator. A method for obtaining equivalent nominal performance forboth control designs is introduced. An experimentally verified benchmark model with industrialrelevance is used for comparing the designs. Results do not show any conclusive advantages ofFeedback linearization.

• 9.
Universidade Federal de Minas Gerais, Brazil.
Robotics and Motion Division, ABB AB. Robotics and Motion Division, ABB AB. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Failure detection in robotic arms using  statistical modeling, machine learning and hybrid gradient boosting2018Report (Other academic)

Modeling and failure prediction is an important task in manyengineering systems. For this task, the machine learning literaturepresents a large variety of models such as classification trees,random forest, artificial neural networks, fuzzy systems, amongothers. In addition, standard statistical models can be applied suchas the logistic regression, linear discriminant analysis, $k$-nearestneighbors, among others. This work evaluates advantages andlimitations of statistical and machine learning methods to predictfailures in industrial robots. The work is based on data from morethan five thousand robots in industrial use. Furthermore, a newapproach combining standard statistical and machine learning models,named \emph{hybrid gradient boosting}, is proposed. Results show thatthe a priori treatment of the database, i.e., outlier analysis,consistent database analysis and anomaly analysis have shown to becrucial to improve classification performance for statistical, machinelearning and hybrid models. Furthermore, local joint information hasbeen identified as the main driver for failure detection whereasfailure classification can be improved using additional informationfrom different joints and hybrid models.

• 10.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Automatic Control Education in a CDIO Perspective2017In: 20th IFAC World Congress, Elsevier, 2017, Vol. 50(1), no 1, p. 12161-12166Conference paper (Refereed)

The CDIO framework for development of engineering education is presented, including the overall ideas, the fundamental documents, and some development tools. The automatic control subject and its role in engineering education is studied using the CDIO Standards as reference. Some examples from the engineering education at Linkoping University are presented with special focus on the control education. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

• 11.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
ABB Robotics, Sweden. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. ABB Robotics, Sweden. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Industrial Robot Tool Position Estimation using Inertial Measurements in a Complementary Filter and an EKF2017In: 20th IFAC World Congress, Elsevier, 2017, Vol. 50, p. 12748-12752Conference paper (Refereed)

In this work an Inertial Measurement Unit is used to improve tool position estimates for an ABB IRB 4600 industrial robot, starting from estimates based on motor angle forward kinematics. A Complementary Filter and an Extended Kalman Filter are investigated. The Complementary Filter is found to perform on par with the Extended Kalman Filter while having lower complexity both in the tuning process and the filtering computations.

• 12.
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. 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.
Platform for Teaching Sensor Fusion Using a Smartphone2017In: International journal of engineering education, ISSN 0949-149X, Vol. 33, no 2B, p. 781-789Article in journal (Refereed)

A platform for sensor fusion consisting of a standard smartphone equipped with the specially developed Sensor Fusion appis presented. The platform enables real-time streaming of data over WiFi to a computer where signal processingalgorithms, e.g., the Kalman filter, can be developed and executed in a Matlab framework. The platform is an excellenttool for educational purposes and enables learning activities where methods based on advanced theory can be implementedand evaluated at low cost. The article describes the app and a laboratory exercise developed around these new technologicalpossibilities. The laboratory session is part of a course in sensor fusion, a signal processing continuation course focused onmultiple sensor signal applications, where the goal is to give the students hands on experience of the subject. This is done byestimating the orientation of the smartphone, which can be easily visualized and also compared to the built-in filters in thesmartphone. The filter can accept any combination of sensor data from accelerometers, gyroscopes, and magnetometers toexemplify their importance. This way different tunings and tricks of important methods are easily demonstrated andevaluated on-line. The presented framework facilitates this in a way previously impossible.

• 13.
Military Institute of Engineering, Brazil.
Military Institute of Engineering, Brazil. Military Institute of Engineering, Brazil. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Introducing CDIO at The Military Institue of Engineering in Brazil2016Report (Other academic)

This report describes the motivation, the current state and the future actions of an improvement process in engineering education at the Brazilian higher education institution called the Military Institute of Engineering. Based on the reasons for why and how to change, the CDIO framework was chosen, at the end of 2014, as the kernel of this improvement process. The activities realized, the plan for the future actions and the open questions are presented in this report.

• 14.
Military Institute of Engineering, Brazil.
Military Institute of Engineering, Brazil. Military Institute of Engineering, Brazil. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Introduction of the CDIO Framework ay the Military Institue of Engineering in Brazil2016In: The 12th International CDIO Conference Proceedings – Full papers / [ed] Jerker Björkqvist, Kristina Edström, Ronald J. Hugo, Juha Kontio, Janne Roslöf, Rick Sellens & Seppo Virtanen, 2016Conference paper (Refereed)

This paper describes the motivation, the current state and the further actions of an improvement process of the engineering education at the Military Institute of Engineering (IME) in Brazil. Based on the reasons for why and how to change, the CDIO framework has been chosen as the kernel of this improvement process. The activities realized the plan of the further actions and the open questions are presented in this paper. The paper is a condensed presentation of the report (Cerqueira et. al., 2016), where a thorough background and more details can be found.

• 15.
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. 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.
IO (Implement and Operate) First in an Automatic Control Context2016In: Proceedings of the 12th International CDIO Conference, Turku University of Applied Sciences,Turku, Finland, June 12-16, 2016 / [ed] Jerker Björkqvist, Kristina Edström, Ronald J. Hugo, Juha Kontio, Janne Roslöf, Rick Sellens & Seppo Virtanen, CDIO , 2016, p. 238-249Conference paper (Refereed)

A first course in Automatic control is presented.  A main objective of the course is to put most of the emphasis on the Implement and Operate phases in the process of developing a control system for a process. The course is built around a large amount of student active learning based on three extensive laboratory exercises, where each laboratory exercise can have duration of up to two weeks. For each of the laboratory exercises there is a sequence of learning activities supporting the students’ learning: Introductory lecture, problem solving session, preparation work, help-desk session, independent work in the laboratory, and a final demonstration of the control system. In addition there is a small project where the task is to write a manual for a process operator. The laboratory tasks involve implementation of a control system in an industrial PLC (Programmable Logic Controller) and development of an operator interface.

• 16.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
ABB Corporate Research, Västerås, Sweden. ABB Corporate Research, Västerås, Sweden. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. ABB Robotics, Västerås, Sweden.
A data-driven approach to diagnostics of repetitive processes in the distribution domain: Applications to gearbox diagnosticsin industrial robots and rotating machines2014In: Mechatronics (Oxford), ISSN 0957-4158, E-ISSN 1873-4006, Vol. 24, no 8, p. 1032-1041Article in journal (Refereed)

This paper presents a data-driven approach to diagnostics of systems that operate in a repetitive manner. Considering that data batches collected from a repetitive operation will be similar unless in the presence of an abnormality, a condition change is inferred by comparing the monitored data against an available nominal batch. The method proposed considers the comparison of data in the distribution domain, which reveals information of the data amplitude. This is achieved with the use of kernel density estimates and the Kullback–Leibler distance. To decrease sensitivity to disturbances while increasing sensitivity to faults, the use of a weighting vector is suggested which is chosen based on a labeled dataset. The framework is simple to implement and can be used without process interruption, in a batch manner. The approach is demonstrated with successful experimental and simulation applications to wear diagnostics in an industrial robot gearbox and for diagnostics of gear faults in a rotating machine.

• 17.
Lund University, Sweden. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Lund University, Sweden. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Estimation-based ILC applied to a parallel kinematic robot2014In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 33Article in journal (Refereed)

Estimation-based iterative learning control (ILC) is applied to a parallel kinematic manipulator known as the Gantry-Tau parallel robot. The system represents a control problem where measurements of the controlled variables are not available. The main idea is to use estimates of the controlled variables in the ILC algorithm, and in the paper this approach is evaluated experimentally on the Gantry-Tau robot. The experimental results show that an ILC algorithm using estimates of the tool position gives a considerable improvement of the control performance. The tool position estimate is obtained by fusing measurements of the actuator angular positions with measurements of the tool path acceleration using a complementary filter.

• 18.
ABB Corporate Research, Västerås, Sweden.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. ABB Corporate Research, Västerås, Sweden. ABB Corporate Research, Västerås, Sweden. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. ABB Robotics, Västerås, Sweden. ABB Robotics, Västerås, Sweden. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Simulation based Evaluation of Fault Detection Algorithms: Applications to Wear Diagnosis in Manipulators2014In: Proceedings of the 19th IFAC World Congress, 2014Conference paper (Refereed)

Fault detection algorithms (FDAs) process data to generate a test quantity. Test quantities are used to determine presence of a fault in a monitored system, despite disturbances. Because only limited knowledge of the system can be embedded in an FDA, it is important to evaluate it in scenarios relevant in practice. In this paper, simulation based approaches are proposed in an attempt to determine: i) which disturbances affect the output of an FDA the most; ii) how to compare the performance of dierent FDAs; and iii) which combinations of fault change size and disturbances variations are allowed to achieve satisfactory performance. The ideas presented are inspired by the literature of design of experiments, surrogate models, sensitivity analysis and change detection. The approaches are illustrated for the problem of wear diagnosis in manipulators where three FDAs are considered. The application study reveals that disturbances caused by variations in temperature and payload mass error affect the FDAs the most. It is also shown how the size of these disturbances delimit the capacity of an FDA to relate to wear changes. Further comparison of the FDAs reveal which performs "best" in average.

• 19.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
ABB Corporate Research. ABB Corporate Research. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
A Data-Driven Method for Monitoring of Repetitive Systems: Applications to Robust Wear Monitoring of a Robot Joint2013Report (Other academic)

This paper presents a method for monitoring of systems that operate in a repetitive manner. Considering that data batches collected from a repetitive operation will be similar unless in the presence of an abnormality, a condition change is inferred by comparing the monitored data against a nominal batch. The method proposed considers the comparison of data in the distribution domain, which reveals information of the data amplitude. This is achieved with the use of kernel density estimates and the Kullback-Leibler distance. To decrease sensitivity to unknown disturbances while increasing sensitivity to faults, the use of a weighting vector is suggested which is chosen based on a labeled dataset. The framework is simple to implement and can be used without process interruption, in a batch manner. The method was developed with interests in industrial robotics where a repetitive behavior is commonly found. The problem of wear monitoring in a robot joint is studied based on data collected from a test-cycle. Real data from accelerated wear tests and simulations are considered. Promising results are achieved where the method output shows a clear response to the wear increases.

• 20.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Analysis of boundary effects in iterative learning control2013In: International Journal of Control, ISSN 0020-7179, E-ISSN 1366-5820, Vol. 86, no 3, p. 410-415Article in journal (Refereed)

Boundary effects in iterative learning control (ILC) algorithms are considered in this article. ILC algorithms involve filtering of input and error signals over finite-time intervals, often using non-causal filters, and it is important that the boundary effects of the filtering operations are handled in an appropriate way. The topic is studied using both a proposed theoretical framework and simulations, and it is shown that the method for handling the boundary effects has impact on the stability and convergence properties of the ILC algorithm.

• 21.
Linköping University, Department of Electrical Engineering, Computer Engineering. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
A Design-Build-Test course in electronics based on the CDIO framework for engineering education2012In: International Journal of Electrical Engineering Education, ISSN 0020-7209, E-ISSN 2050-4578, Vol. 49, no 4, p. 349-364Article in journal (Refereed)

A Design-Build-Test (DBT) course in electronics is presented. The course is designed based on the CDIO (Conceive-Design-Implement-Operate) framework for engineering education. It is part of the curriculum of two engineering programs at Linköping University, Sweden, where it has been given successfully for a number of years. The cornerstones of the course consist of carefully designed learning outcomes based on the CDIO Syllabus, a structured project management model such that the project tasks are carried out according to professional and industry-like routines, with well-designed organisation of the staff supporting the course, and challenging project tasks.

• 22.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Static Friction in a Robot Joint: Modeling and Identification of Load and Temperature Effects2012In: Journal of Dynamic Systems Measurement, and Control, ISSN 0022-0434, E-ISSN 1528-9028, Vol. 134, no 5Article in journal (Refereed)

Friction is the result of complex interactions between contacting surfaces in down to a nanoscale perspective. Depending on the application, the different models available are more or less suitable. Static friction models are typically considered to be dependent only on relative speed of interacting surfaces. However, it is known that friction can be affected by other factors than speed.

In this paper, the typical friction phenomena and models used in robotics are reviewed. It is shown how such models can be represented as a sum of functions of relevant states which are linear and nonlinear in the parameters, and how the identification method described in Golub and Pereyra (1973) can be used to identify them when all states are measured. The discussion follows with a detailed experimental study of friction in a robot joint under changes of joint angle, load torque and temperature. Justified by their significance, load torque and temperature are included in an extended static friction model. The proposed model is validated in a wide operating range, considerably improving the prediction performance compared to a standard model.

• 23.
Linköping University, Department of Electrical Engineering, Computer Engineering. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Teaching Project Courses in Large Scale Using Industry Like Methods - Experiences After Ten Years2012Conference paper (Other academic)

A Design-Build-Test (DBT) project course in electronics is presented. The course was developed during the first years of the CDIO Initiative, and it has been given successfully for almost ten years within two engineering programs at Linköping University. More than 2000 students have passed the course, and it is considered to be one of the most popular and also demanding courses within these programs. The key factors that have contributed to the success of the course are:

• Clearly defined learning outcomes.
• A suitable and well working course organization.
• A systematic method for project management.
• Challenging project tasks of sufficient complexity.
• Laboratory workspaces with modern equipment and high availability.

The aim of the paper is to describe these key factors in more detail based on the experiences that have been gained during the almost ten years the course has been given.

• 24.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
A Framework for Analysis of Observer-Based ILC2011In: Asian journal of control, ISSN 1561-8625, E-ISSN 1561-8625, Vol. 13, no 1, p. 3-14Article in journal (Refereed)

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.

• 25.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Lund University, Sweden. Lund University, Sweden. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Observer-based ILC Applied to the Gantry-Tau Parallel Kinematic Robot2011In: Proceedings of the 18th IFAC World Congress, IFAC , 2011, p. 992-998Conference paper (Refereed)

Three approaches of iterative learning control (ILC) applied to a Gantry-Tau parallel kinematic robot are studied; ILC algorithms using 1) measured motor angles, 2) tool-position estimates, and for evaluation purposes, 3) measured tool position. The approaches are compared experimentally, with the tool performance evaluated using external sensors. It is concluded that the tool performance can be improved using tool-position estimates in the ILC algorithm, compared to when using motor-angle measurements. Applying ILC algorithms to a system following trajectories with so-called lead-in/lead-out is also considered in the paper.

• 26.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Static Friction in a Robot Joint: Modeling and Identificaiton of Load and Temperature Effects2011Report (Other academic)

Friction is the result of complex interactions between contacting surfaces in a nanoscale perspective. Depending on the application, the different models available are more or less suitable. Available static friction models are typically considered to be dependent only on relative speed of interacting surfaces. However, it is known that friction can be affected by other factors than speed. In this paper, the typical friction phenomena and models used in robotics are reviewed. It is shown how such models can be represented as a sum of linear and nonlinear functions of relevant states, and how the identiﬁcation method described in [1] can be used to identify them when all state sare measured. The discussion follows with a detailed experimental study of friction in a robot joint under changes of joint angle, load torque and temperature. Justiﬁed by their signiﬁcance, load torque and temperature are included in an extended static friction model. The proposed model is validated in a wide operating range, considerably improving the prediction performance compared to a standard model.

• 27.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
A Framework for Analysis of Observer-Based ILC2010In: Proceedings of Reglermöte 2010, 2010Conference paper (Other academic)

A framework for Iterative Learning Control (ILC) is derived when the ILC algorithm is based on estimates from an observer. 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 discussed when the ILC algorithm is based on different errors and it is exempliﬁed by an ILC algorithm applied to a ﬂexible two-mass model of a robot joint.

• 28.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Management and Engineering, Project management, Innovations and Entrepreneurship . Linköping University, The Institute of Technology. Linköping University, Department of Management and Engineering, Project management, Innovations and Entrepreneurship . Linköping University, The Institute of Technology.
Integrating Entrepreneurship in DBT Project Courses at Linköping University2010In: Proceedings of the 6th International CDIO Conference, 2010Conference paper (Other academic)

An example of how entrepreneurship can be integrated into Design-Build-Test (DBT) project courses is presented. The example is taken from the engineering program Applied Physics and Electrical Engineering at Linköping University, where entrepreneurship has been introduced in ten different DBT project courses related to the specializations of the program. The purpose of the entrepreneurship part is that the students shall acquire knowledge and abilities within the general area of entrepreneurship with particular focus on business planning for new ventures. The organization and execution of the entrepreneurship activities are described in detail together with a summary of the experiences from the first year. The results of the entrepreneurship activities within the project courses are positive and will be further developed, with emphasis on the connections between project ideas and technical contents of the courses.

• 29.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Lund University, Sweden. Lund University, Sweden. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Observer-Based ILC Applied to the Gantry-Tau Parallel Kinematic Robot: Modelling, Design and Experiments2010Report (Other academic)

Three different approaches of iterative learning control (ILC) applied to a parallel kinematic robot are studied. First, the ILC algorithm is based on measured motor angles only. Second, tool-position estimates are used in the ILC algorithm. For evaluation, the ILC algorithm finally is based on measured tool position. Model-based tuning of the ILC filters enables learning above the resonance frequencies of the system. The approaches are compared experimentally on a Gantry-Tau prototype, with the tool performance being evaluated by using external sensors. It is concluded that the tool performance can be improved by using tool-position estimates in the ILC algorithm, compared to when using motor-angle measurements. In the paper applying ILC algorithms to a system following trajectories with so-called lead-in/lead-out is also considered, as well as dynamic modelling of the Gantry-Tau prototype.

• 30.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Some Implementation Aspects of Iterative Learning Control2010Report (Other academic)

Some implementation aspects of Iterative Learning Control (ILC) are considered. Since the ILC algorithm involves ﬁltering of various signals over ﬁnite time intervals, often using non-causal ﬁlters, it is important that the boundary eﬀects of the ﬁltering operations are handled in an appropriate way when implementing the ILC algorithm. The paper illustrates in both theoretical analysis using the matrix description and in simulations of a twomass system that the method of implementation for handling the boundary eﬀects can have large inﬂuence over stability and convergence properties of the ILC algorithm.

• 31.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
ABB AB, Sweden. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
A Benchmark Problem for Robust Feedback Control of a Flexible Manipulator2009In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 17, no 6, p. 1398-1405Article in journal (Refereed)

A benchmark problem for robust feedback control of a flexible manipulator is presented. The system to be controlled is a four-mass system subject to input saturation, nonlinear gear elasticity, model uncertainties, and load disturbances affecting both the motor and the arm. The system should be controlled by a discrete-time controller that optimizes performance for given robustness requirements. Four suggested solutions are presented, and even though the solutions are based on different design methods, they give comparable results.

• 32.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
A Framework for Analysis of Observer-Based ILC2009In: Proceedings of Symposium on Learning Control at IEEE CDC, 2009Conference paper (Refereed)

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.

• 33.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
A Framework for Analysis of Observer-Based ILC2009Report (Other academic)

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.

• 34.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
ILC Applied to a Flexible Two-Link Robot Model using Sensor-Fusion-Based Estimates2009In: Proceedings of 48th IEEE Conference on Decision and Control, IEEE , 2009, p. 458-463Conference paper (Refereed)

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.

• 35.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
ILC Applied to a Flexible Two-Link Robot Model using Sensor-Fusion-Based Estimates2009Report (Other academic)

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.

• 36.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Performance of ILC Applied to a Flexible Mechanical System2009In: Proceedings of European Control Conference (ECC), 2009, p. 1511-1516Conference paper (Refereed)

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.

• 37.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, The Institute of Technology. Linköping University, The Institute of Technology. Linköping University, The Institute of Technology. Technical University of Denmark, Denmark. Technical University of Denmark, Denmark.
Using the CDIO Syllabus in Formulation of Program Goals - Experiences and Comparisons2009In: Proceedings of the 5th International CDIO Conference, 2009Conference paper (Refereed)

This paper presents experiences and results from large scale and systematic use of the CDIO Syllabus for developing program goals and formulating learning outcomes at Linköping University (LiU), Sweden, and Technical University of Denmark (DTU). The approaches are based on the use of tools for program design such as ITU-matrices and skill progression matrices. During the process local adaptations of the Syllabus have been made in order to meet regulations by authorities in higher education as well as to cover programs in related areas as natural sciences. The experiences are that the CDIO Syllabus is a very useful tool in this process and that the way of organizing the management of the education programs is important for success as well as support from students, faculty members and stakeholders.

• 38.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
ABB AB, Crane Systems, Sweden. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
A Benchmark Problem for Robust Control of a Multivariable Nonlinear Flexible Manipulator2008In: Proceedings of the 17th IFAC World Congress, 2008, p. 1206-1211Conference paper (Refereed)

A benchmark problem for robust feedback control of a manipulator is presented. The system to be controlled is an uncertain nonlinear two link manipulator with elastic gear transmissions. The gear transmission is described by nonlinear friction and elasticity. The system is uncertain according to a parametric uncertainty description and due to uncertain disturbances affecting both the motors and the tool. The system should be controlled by a discrete-time controller that optimizes performance for given robustness requirements. The control problem concerns only disturbance rejection. The proposed model is validated by experiments on a real industrial manipulator.

• 39.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
ABB AB, Crane Systems, Sweden. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
A Benchmark Problem for Robust Control of a Multivariable Nonlinear Flexible Manipulator2008Report (Other academic)

A benchmark problem for robust feedback control of a manipulator is presented. The system to be controlled is an uncertain nonlinear two link manipulator with elastic gear transmissions. The gear transmission is described by nonlinear friction and elasticity. The system is uncertain according to a parametric uncertainty description and due to uncertain disturbances affecting both the motors and the tool. The system should be controlled by a discrete-time controller that optimizes performance for given robustness requirements. The control problem concerns only disturbance rejection. The proposed model is validated by experiments on a real industrial manipulator.

• 40.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Arm-Side Evaluation of ILC Applied to a Six-Degrees-of-Freedom Industrial Robot2008In: Proceedings of the 17th IFAC World Congress, 2008, , p. 13450-13455p. 13450-13455Conference paper (Refereed)

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.

• 41.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Comparison of Performance and Robustness for two Classical ILC Algorithms Applied to a Flexible System2008Report (Other academic)

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.

• 42.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Computer Supported Learning and Assessment in Engineering Education2008In: Proceedings of the 4th International CDIO Conference, 2008Conference paper (Refereed)

Some aspects of computer support in learning and assessment in engineering education are discussed. It is emphasized that the use of computer support, like e.g. simulations, computations, visualizations, has to be closely connected to the formulation of the expected learning outcomes and the assessment methods. Some examples of computer support are related to the CDIO Syllabus. Some experiences from more that two decades of computer aided learning and assessment within the Division of Automatic Control at Linköping University are presented

• 43.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Estimation of Nonlinear Effects in Frequency Domain Identification of Industrial Robots2008In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 57, no 4, p. 856-863Article in journal (Refereed)

A method for the detection and estimation of nonlinear distortions when identifying multivariable frequency response functions (FRF) is considered. The method is successfully applied to experimental data from an industrial robot, collected in closed loop. The results show that nonlinear distortions are indeed present and cause larger variability in the FRF than the measurement noise contributions.

• 44.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Estimation of nonlinear effects in frequency-domain identification of industrial robots2008In: IEEE Transactions on Instrumentation and Measurement, Braunschweig, Germany, 2008, p. 856-863Conference paper (Other academic)

A method for the detection and estimation of nonlinear distortions when identifying multivariable frequency response functions (FRF) is considered. The method is successfully applied to experimental data, which were collected in closed loop, from an industrial robot. The results show that nonlinear distortions are indeed present and cause larger variability in the FRF than the measurement-noise contributions.

• 45.
Chalmers University of Technology, Sweden.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Technical University of Denmark, Denmark.
Faculty Professional Competence Development Programs - Comparing Approaches from Three Universities2008In: Proceedings of the 4th International CDIO Conference, 2008Conference paper (Refereed)

This paper describes faculty professional competence development programs at Chalmers University of Technology, the Technical University of Denmark, and Linköping University. Examples of professional competences include project management, communication, teamwork and organizational change management. The description of the programs is complemented by interviews with faculty aiming at clarifying the needs for and experiences from faculty professional competences development programs.

• 46.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, The Institute of Technology.
Framstående utbildningsmiljö - Hur blir man det?2008In: Nätverket Ingenjörsutbildningarnas Utvecklingskonferens 2008, 2008Conference paper (Other academic)

Studierektorsområdet Reglersystem vid LiTH (Tekniska Högskolan vid Linköpings universitet) tilldelades 2007 utmärkelsen Framstående Utbildningsmiljö av Högskoleverket. Utmärkelsen gavs till sammanlagt fem utbildningsmiljöer, varav två är verksamma inom ingenjörsutbildning. I detta bidrag avser vi att redogöra för denna process och de faktorer som främst bidrog till att området Reglersystem fick denna utmärkelse. De faktorer som lyfts fram som centrala för att åstadkomma en god utbildningskvalité är framför allt att ha tydliga mål för utbildningen, en gedigen ämnesmässig grund, en väl fungerande organisation och positiv attityd bland alla medverkande samt former för lärande och examination som är anpassade till utbildningens mål.

• 47.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Performance and Robustness of ILC Applied to Flexible Systems2008In: Proceedings of Reglermöte 2008, 2008, p. 210-216Conference paper (Other academic)

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.

• 48.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Performance of ILC Applied to a Flexible Mechanical System2008Report (Other academic)

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.

• 49.
Linköping University, Department of Computer and Information Science, RTSLAB - Real-Time Systems Laboratory. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. Software Engineering Institute, Carnegie Mellon University, Pittsburgh, PA, USA. University of Virginia, USA.
Quantifying and Suppressing the Measurement Disturbance in Feedback Controlled Real-Time Systems2008In: Real-time systems, ISSN 0922-6443, E-ISSN 1573-1383, Vol. 40, no 1, p. 44-76Article in journal (Refereed)

In the control of continuous and physical systems, the controlled system is sampled sufficiently fast to capture the dynamics of the system. In general, this property cannot be applied to the control of computer systems as the measured variables are often computed over a data set, e.g., deadline miss ratio. In this paper we quantify the disturbance present in the measured variable as a function of the data set size and the sampling period, and we propose a feedback control structure that suppresses the measurement disturbance. The experiments we have carried out show that a controller using the proposed control structure outperforms a traditional control structure with regard to performance reliability.

• 50.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
ABB AB, Sweden. Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
A Benchmark Problem for Robust Feedback Control of a Flexible ManipulatorA benchmark problem for robust feedback control of a flexible manipulator2007Report (Other (popular science, discussion, etc.))

A benchmark problem for robust feedback control of a flexible manipulator is presented. The system to be controlled is a four-mass system subject to input saturation, nonlinear gear elasticity, model uncertainties, and load disturbances affecting both the motor and the arm. The system should be controlled by a discrete-time controller that optimizes performance for given robustness requirements. Four suggested solutions are presented, and even though the solutions are based on different design methods, they give comparable results.

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