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  • 1.
    Axelsson, Patrik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Norrlöf, Mikael
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wernholt, Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gustafsson, Fredrik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Extended Kalman Filter Applied to Industrial Manipulators2010In: Proceedings of Reglermöte 2010, 2010Conference paper (Other academic)
    Abstract [en]

    This paper summarizes previous work on tool position estimation on industrial manipulators, and emphasize the problems that must be taken care of in order to get a satisfied result. The acceleration of the robot tool, measured by an accelerometer, together with measurements of motor angles are used. The states are estimated with an extended Kalman filter. A method for tuning the covariance matrices for the noise, used in the observer, is suggested. The work has been focused on a robot with two degrees of freedom.

  • 2.
    Carvalho Bittencourt, André
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wernholt, Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Sander-Tavallaey, Shiva
    ABB, Sweden.
    Brogårdh, Torgny
    ABB, Sweden.
    An Extended Friction Model to capture Load and Temperature effects in Robot Joints2010In: Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010, p. 6161-6167Conference paper (Refereed)
    Abstract [en]

    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, static friction in robot joints is studied with respect to changes in joint angle, load torque and temperature. The effects of these variables are analyzed by means of experiments on a standard industrial robot. 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, reducing the average error a factor of 6 when compared to a standard static friction model.

  • 3.
    Carvalho Bittencourt, André
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wernholt, Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Shiva, Sander-Tavallaey
    ABB, Sweden.
    Brogårdh, Torgny
    ABB, Sweden.
    An Extended Friction Model to Capture Load and Temperature Effects in Robot Joints2010Report (Other academic)
    Abstract [en]

    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, static friction in robot joints is studied with respect to changes in joint angle, load torque and temperature. The effects of these variables are analyzed by means of experiments on a standard industrial robot. 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, reducing the average error a factor of 6 when compared to a standard static friction model.

  • 4.
    Enqvist, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Norrlöf, Mikael
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wernholt, Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    The CDIO Initiative from an Automatic Control Project Course Perspective2005In: Proceedings of the 16th IFAC World Congress, 2005, p. 2283-2283Conference paper (Refereed)
    Abstract [en]

    The CDIO (Conceive Design Implement Operate) Initiative is explained, and some of the results at the Applied Physics and Electrical Engineering program at Linköping University, Sweden, are presented. A project course in Automatic Control is used as an example. The projects within the course are carried out using the LIPS (Linköping interactive project steering) model. An example of a project, the golf playing industrial robot, and the results from this project are also covered.

  • 5.
    Enqvist, Martin
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Norrlöf, Mikael
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wernholt, Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hansson, Anders
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    The CDIO Initiative from an Automatic Control Project Course Perspective2004Report (Other academic)
    Abstract [en]

    The CDIO (Conceive Design Implement Operate) Initiative is explained, and some of the results at the Applied Physics and Electrical Engineering program at Linköping University, Sweden, are presented. A project course in Automatic Control is used as an example. The projects within the course are carried out using the LIPS (Linköping interactive project steering) model. An example of a project, the golf playing industrial robot, and the results from this project are also covered.

  • 6.
    Gunnar, Johan
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wernholt, Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hovland, Geir
    The University of Queensland, Australia.
    Brogårdh, Torgny
    ABB Automation Technologies, Robotics, Sweden.
    Nonlinear Grey-box Identification of Linear Actuators Containing Hysteresis2006In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation, 2006, p. 1818-1823Conference paper (Refereed)
    Abstract [en]

    A new identification procedure for a linear actuator used in parallel kinematic manipulators has been developed. The actuator dynamics contain both hysteresis and backlash resulting in a highly nonlinear system. The results in this paper show that not only can a nonlinear model of the system be successfully identified from measurement data, but the model is also compact enough to be an ideal candidate for inclusion in a high-performance robot control system.

  • 7.
    Gunnar, Johan
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wernholt, Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hovland, Geir
    The University of Queensland, Australia.
    Brogårdh, Torgny
    ABB Automation Technologies, Robotics, Sweden.
    Nonlinear Grey-box Identification of Linear Actuators Containing Hysteresis2006Report (Other academic)
    Abstract [en]

    A new identification procedure for a linear actuator used in parallel kinematic manipulators has been developed. The actuator dynamics contain both hysteresis and backlash resulting in a highly nonlinear system. The results in this paper show that not only can a nonlinear model of the system be successfully identified from measurement data, but the model is also compact enough to be an ideal candidate for inclusion in a high-performance robot control system.

  • 8.
    Henriksson, Robert
    et al.
    McKinsey & Company, Sweden.
    Norrlöf, Mikael
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Moberg, Stig
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wernholt, Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Experimental Comparison of Observers for Tool Position Estimation of Industrial Robots2009Report (Other academic)
    Abstract [en]

    This paper investigates methods for tool position estimation of industrial robots. It is assumed that the motor angular position and the tool acceleration are measured. The considered observers are different versions of the extended Kalman filter as well as a deterministic observer. A method for tuning the observers is suggested and the robustness of the methods is investigated. The observers are evaluated experimentally on a commercial industrial robot.

  • 9.
    Moberg, Stig
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wernholt, Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hanssen, Sven
    Royal Institute of Technology, Department of Solid Mechanics.
    Brogårdh, Torgny
    ABB AB, Sweden.
    Modeling and Parameter Estimation of Robot Manipulators using Extended Flexible Joint Models2014In: Journal of Dynamic Systems Measurement, and Control, ISSN 0022-0434, E-ISSN 1528-9028, Vol. 136, no 3, p. 031005-Article in journal (Refereed)
    Abstract [en]

    This paper considers the problem of dynamic modeling and identification of robot manipulators with respect to their elasticities. The so-called flexible joint model, modeling only the torsional gearbox elasticity, is shown to be insufficient for modeling a modern industrial manipulator accurately. The extended flexible joint model, where non-actuated joints are added to model the elasticity of the links and bearings, is used to improve the model accuracy. The unknown elasticity parameters are estimated using a frequency domain gray-box identification method. The conclusion is that the obtained model describes the movements of the motors and the tool mounted on the robot with significantly higher accuracy. Similar elasticity model parameters are obtained when using two different output variables for the identification, the motor position and the tool acceleration.

  • 10.
    Norrlöf, Mikael
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Henriksson, Robert
    McKinsey & Company, Sweden.
    Moberg, Stig
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wernholt, Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Schön, Thomas
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Experimental Comparison of Observers for Tool Position Estimation of Industrial Robots2009In: Proceedings of the 48th IEEE Conference on Decision and Control, 2009, p. 8065-8070Conference paper (Refereed)
    Abstract [en]

    This paper investigates methods for tool position estimation of industrial robots. It is assumed that the motor angular position and the tool acceleration are measured. The considered observers are different versions of the extended Kalman filter as well as a deterministic observer. A method for tuning the observers is suggested and the robustness of the methods is investigated. The observers are evaluated experimentally on a commercial industrial robot.

  • 11.
    Wernholt, Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Multivariable Frequency-Domain Identification of Industrial Robots2007Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Industrial robots are today essential components in the manufacturing industry where they are used to save costs, increase productivity and quality, and eliminate dangerous and laborious work. High demands on accuracy and speed of the robot motion require that the mathematical models, used in the motion control system, are accurate. The models are used to describe the complicated nonlinear relation between the robot motion and the motors that cause the motion. Accurate dynamic robot models are needed in many areas, such as mechanical design, performance simulation, control, diagnosis, and supervision.

    A trend in industrial robots is toward lightweight robot structures, where the weight is reduced but with a preserved payload capacity. This is motivated by cost reduction as well as safety issues, but results in a weaker (more compliant) mechanical structure with enhanced elastic effects. For high performance, it is therefore necessary to have models describing these elastic effects.

    This thesis deals with identification of dynamic robot models, which means that measurements from the robot motion are used to estimate unknown parameters in the models. The measured signals are angular position and torque of the motors. Identifying robot models is a challenging task since an industrial robot is a multivariable, nonlinear, unstable, and resonant system. In this thesis, the unknown parameters (typically spring-damper pairs) in a physically parameterized nonlinear dynamic model are identified, mainly in the frequency domain, using estimates of the nonparametric frequency response function (FRF) in different robot configurations/positions. Each nonparametric FRF then describe the local behavior around an operating point. The nonlinear parametric robot model is linearized in the same operating points and the optimal parameters are obtained by minimizing the discrepancy between the nonparametric FRFs and the parametric FRFs (the FRFs of the linearized parametric robot model).

    Methods for estimating the nonparametric FRF from experimental data are analyzed with respect to bias, variance, and nonlinearities. In order to accurately estimate the nonparametric FRF, the experiments must be carefully designed. To minimize the uncertainty in the estimated parameters, the selection of optimal robot configurations/positions for the experiments is also part of the design. Different parameter estimators are compared in the thesis and experimental results show the usefulness of the proposed identification procedure. The identified nonlinear robot model gives a good global description of the dynamics in the frequency range of interest.

    The research work is also implemented and made easily available in a software tool for accurate estimation of nonparametric FRFs as well as parametric robot models.

    List of papers
    1. Frequency-Domain Gray-Box Identification of Industrial Robots
    Open this publication in new window or tab >>Frequency-Domain Gray-Box Identification of Industrial Robots
    2008 (English)In: Proceedings of the 17th IFAC World Congress, 2008, p. 15372-15380Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper considers identification of unknown parameters in elastic dynamic models of industrial robots. Identifying such models is a challenging task since an industrial robot is a multivariable, nonlinear, resonant, and unstable system. Unknown parameters (mainly spring-damper pairs) in a physically parameterized nonlinear dynamic model are identified in the frequency domain, using estimates of the nonparametric frequency response function (FRF) in different robot configurations/positions. The nonlinear parametric robot model is linearized in the same positions and the optimal parameters are obtained by minimizing the discrepancy between the nonparametric FRFs and the parametric FRFs (the FRFs of the linearized parametric robot model). In order to accurately estimate the nonparametric FRFs, the experiments must be carefully designed. The selection of optimal robot configurations for the experiments is also part of the design. Different parameter estimators are compared and experimental results show the usefulness of the proposed identification procedure. The weighted logarithmic least squares estimator achieves the best result and the identified model gives a good global description of the dynamics in the frequency range of interest.

    Keywords
    System identification, Multivariable systems, Nonlinear systems, closed-loop identification, frequency response methods
    National Category
    Engineering and Technology Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-12711 (URN)10.3182/20080706-5-KR-1001.02600 (DOI)978-3-902661-00-5 (ISBN)
    Conference
    17th IFAC World Congress, Seoul, South Korea, July, 2008
    Available from: 2007-10-30 Created: 2007-10-30 Last updated: 2013-02-23
    2. Analysis of Methods for Multivariable Frequency Response Function Estimation in Closed Loop
    Open this publication in new window or tab >>Analysis of Methods for Multivariable Frequency Response Function Estimation in Closed Loop
    2007 (English)In: Proceedings of the 46th IEEE Conference on Decision and Control, 2007, p. 4881-4888Conference paper, Published paper (Refereed)
    Abstract [en]

    Estimation methods for the multivariable frequency response function are analyzed, both in open and closed loop. Expressions for the bias and covariance are derived and the usefulness of these expressions is illustrated in simulations of an industrial robot where the different estimators are compared. The choice of estimator depends on the signal-to- noise ratio as well as the measurement setup and a bias-variance trade-off.

    Keywords
    Closed loop systems, Frequency response, Industrial robots, Multivariable frequency response function estimation, Open loop, Signal-to-noise ratio, Closed loop
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-12712 (URN)10.1109/CDC.2007.4434780 (DOI)978-1-4244-1497-0 (ISBN)978-1-4244-1498-7 (ISBN)
    Conference
    46th IEEE Conference on Decision and Control, New Orleans, LA, December, 2007
    Available from: 2007-10-30 Created: 2007-10-30 Last updated: 2013-09-15
    3. Experimental Comparison of Methods for Multivariable Frequency Response Function Estimation
    Open this publication in new window or tab >>Experimental Comparison of Methods for Multivariable Frequency Response Function Estimation
    2008 (English)In: Proceedings of the 17th IFAC World Congress, 2008, p. 15359-15366Conference paper, Published paper (Refereed)
    Abstract [en]

    Nonparametric estimation methods for the multivariable frequency response function are experimentally evaluated using closed-loop data from an industrial robot. Three classical estimators (H1, joint input-output, arithmetic mean) and two estimators based on nonlinear averaging techniques (harmonic mean, geometric/logarithmic mean) are considered. The estimators based on nonlinear averaging give the best results, followed by the arithmetic mean estimator, which gives a slightly larger bias. The joint input-output estimator, which is asymptotically unbiased in theory, turns out to give large bias errors for low frequencies. Finally, the H1 estimator gives the largest bias for all frequencies.

    Keywords
    System identification, Frequency response methods, Multivariable systems, Non-parametric identification, Closed-loop identification, Industrial robots
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-12713 (URN)10.3182/20080706-5-KR-1001.02598 (DOI)978-3-902661-00-5 (ISBN)
    Conference
    17th IFAC Worlds Congress, Seoul, Korea, July, 2008
    Available from: 2007-10-30 Created: 2007-10-30 Last updated: 2013-09-15
    4. Estimation of nonlinear effects in frequency-domain identification of industrial robots
    Open this publication in new window or tab >>Estimation of nonlinear effects in frequency-domain identification of industrial robots
    2008 (English)In: IEEE Transactions on Instrumentation and Measurement, Braunschweig, Germany, 2008, p. 856-863Conference paper, Published paper (Other academic)
    Abstract [en]

    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.

    Series
    IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456
    Keywords
    closed loop systems, frequency response, frequency-domain analysis, industrial robots, nonlinear distortion, nonlinear effect estimation, multivariable frequency response function, closed loop, frequency domain identification, nonparametric identification, Frequency response functions (FRF)
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-12714 (URN)10.1109/TIM.2007.911698 (DOI)
    Available from: 2007-10-30 Created: 2007-10-30 Last updated: 2009-05-18
    5. Experiment Design for Identification of Nonlinear Gray-Box Models with Application to Industrial Robots
    Open this publication in new window or tab >>Experiment Design for Identification of Nonlinear Gray-Box Models with Application to Industrial Robots
    2007 (English)In: Proceedings of the 46th IEEE Conference on Decision and Control, 2007, p. 5110-5116Conference paper, Published paper (Refereed)
    Abstract [en]

    Experiment design involving selection of optimal experiment positions for nonlinear gray-box models is studied. From the derived Fisher information matrix, a convex optimization problem is posed. By considering the dual problem, the experiment design is efficiently solved with linear complexity in the number of candidate positions, compared to cubic complexity for the primal problem. In the numerical illustration, using an industrial robot, the parameter covariance is reduced by a factor of six by using the 15 optimal positions compared to using the optimal single position in all experiments.

    Keywords
    Covariance matrices, Industrial robots, matrix algebra, Optimisation, Fisher information matrix, Convex optimization problem, Nonlinear gray-box models, Parameter covariance
    National Category
    Engineering and Technology Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-12715 (URN)10.1109/CDC.2007.4434059 (DOI)978-1-4244-1497-0 (ISBN)978-1-4244-1498-7 (ISBN)
    Conference
    46th IEEE Conference on Decision and Control, New Orleans, LA, USA, December, 2007
    Available from: 2007-10-30 Created: 2007-10-30 Last updated: 2013-02-23
    6. Nonlinear Gray-Box Identification of a Flexible Manipulator
    Open this publication in new window or tab >>Nonlinear Gray-Box Identification of a Flexible Manipulator
    2007 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939Article in journal (Other academic) Published
    Abstract [en]

    A three-step procedure for time-domain nonlinear gray-box identification of an industrial manipulator containing flexibilities is studied. The aim of the first two steps is to obtain good initial values for the third prediction error minimization step. In the first step, rigid body dynamics and friction are identified using a separable least-squares method. In the second step, initial values for flexibilities are obtained using an inverse eigenvalue method. Finally, in the last step, the remaining parameters of a nonlinear graybox model are identified directly in the time domain using prediction error minimization.

    Place, publisher, year, edition, pages
    Elsevier, 2007
    Keywords
    Optimization, Gray-box identification, Flexibility, Minimization, Parameter
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:liu:diva-12716 (URN)
    Available from: 2007-10-30 Created: 2007-10-30 Last updated: 2017-12-14
  • 12.
    Wernholt, Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.
    On Multivariable and Nonlinear Identification of Industrial Robots2004Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The main objective of the thesis is the identification of flexibilities and nonlinearities in mathematical models of industrial robots. In particular, a nonparametric frequency-domain estimation method for the multivariable frequency response function (MFRF) has been evaluated and analyzed for the robot application. Nonlinear gray-box identification has also been treated. Since identification in robotics is a much studied problem, one important part of the thesis also is to give an overview of earlier results.

    For the MFRF estimation method, an approximate expression tor the estimation error has been derived which describes how the estimate is affected by disturbances, the choice of excitation signal, the feedback and the properties of the system itself. The MFRF estimation method has been evaluated using both simulation data and experimental data from an ABB IRB 6600 robot. A number of different aspects regarding excitation signals and averaging techniques have been studied. It is shown, for instance, that the repetitive nature of the disturbances further limits the choice of excitation signals. Averaging the estimates over several periods of data or using experiments with identical excitation does not give any significant reduction due to the repetitive disturbances.

    A three-step identification procedure is also proposed for the combined identification of rigid body dynamics, friction, and flexibilities. The procedure includes continuous-time nonlinear gray-box identification and is exemplified using experimental data.

  • 13.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Analysis of Methods for Multivariable Frequency Response Function Estimation in Closed Loop2007Report (Other academic)
    Abstract [en]

    Estimation methods for the multivariable frequency response function are analyzed, both in open and closed loop. Expressions for the bias and covariance are derived and the usefulness of these expressions is illustrated in simulations of an industrial robot where the different estimators are compared. The choice of estimator depends on the signal-to- noise ratio as well as the measurement setup and a bias-variance trade-off.

  • 14.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Analysis of Methods for Multivariable Frequency Response Function Estimation in Closed Loop2007In: Proceedings of the 46th IEEE Conference on Decision and Control, 2007, p. 4881-4888Conference paper (Refereed)
    Abstract [en]

    Estimation methods for the multivariable frequency response function are analyzed, both in open and closed loop. Expressions for the bias and covariance are derived and the usefulness of these expressions is illustrated in simulations of an industrial robot where the different estimators are compared. The choice of estimator depends on the signal-to- noise ratio as well as the measurement setup and a bias-variance trade-off.

  • 15.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Detection and Estimation of Nonlinear Distortions in Industrial Robots2006In: Proceedings of the 23rd IEEE Instumentation and Measurement Technology Conference, 2006, p. 1913-1918Conference paper (Refereed)
    Abstract [en]

    System identification in robotics often involves the estimation of linear models characterizing the behavior in certain operating points. In this paper, a method for the detection and estimation of nonlinear distortions in an estimated frequency response function (FRF) has successfully been applied to experimental data from an industrial robot. The results show that nonlinear distortions areindeed present and cause larger variability in the FRF than the measurement noise contributions.

  • 16.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Detection and Estimation of Nonlinear Distortions in Industrial Robots2006Report (Other academic)
    Abstract [en]

    System identification in robotics often involves the estimation of linear models characterizing the behavior in certain operating points. In this paper, a method for the detection and estimation of nonlinear distortions in an estimated frequency response function (FRF) has successfully been applied to experimental data from an industrial robot. The results show that nonlinear distortions areindeed present and cause larger variability in the FRF than the measurement noise contributions.

  • 17.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    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)
    Abstract [en]

    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.

  • 18.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    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)
    Abstract [en]

    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.

  • 19.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nonlinear Gray-Box Identification of a Flexible Manipulator2007In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939Article in journal (Other academic)
    Abstract [en]

    A three-step procedure for time-domain nonlinear gray-box identification of an industrial manipulator containing flexibilities is studied. The aim of the first two steps is to obtain good initial values for the third prediction error minimization step. In the first step, rigid body dynamics and friction are identified using a separable least-squares method. In the second step, initial values for flexibilities are obtained using an inverse eigenvalue method. Finally, in the last step, the remaining parameters of a nonlinear graybox model are identified directly in the time domain using prediction error minimization.

  • 20.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nonlinear Grey-Box Identification of Industrial Robots Containing Flexibilities2005In: Proceedings of the 16th IFAC World Congress, 2005, p. 59-59Conference paper (Refereed)
    Abstract [en]

    Nonlinear grey-box identification of industrial robots is considered. A three-step identification procedure is proposed in which parameters for rigid body dynamics, friction and flexibilites can be identified only using measurements on the motor. In the first two steps, good initial parameter estimates are derived which are used in the last step, where the parameters of a nonlinear physically parameterized model are identified directly in the time domain. The procedure is examplifed using real data from an experimental industrial robot.

  • 21.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nonlinear Grey-Box Identification of Industrial Robots Containing Flexibilities2004Report (Other academic)
    Abstract [en]

    Nonlinear grey-box identification of industrial robots is considered. A three-step identification procedure is proposed in which parameters for rigid body dynamics, friction and flexibilites can be identified only using measurements on the motor. In the first two steps, good initial parameter estimates are derived which are used in the last step, where the parameters of a nonlinear physically parameterized model are identified directly in the time domain. The procedure is examplifed using real data from an experimental industrial robot.

  • 22.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, University Library.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nonlinear Identification of a Physically Parameterized Robot Model2006In: Proceedings of the 14th IFAC Symposium on System Identification, 2006, p. 143-148Conference paper (Refereed)
    Abstract [en]

    In the work presented here, a three-step identification procedure for rigid body dynamics, friction, and flexibilities, introduced in (Wernholt and Gunnarsson, 2005), will be utilized and extended. Using the procedure, the parameters can be identified only using motor measurements. In the first step, rigid body dynamics and friction will be identified using a separable least squares method, where a friction model describing the Striebeck effect is used. In the second step, initial values for flexibilities are obtained using inverse eigenvalue theory. Finally, in the last step, the remaining parameters of a nonlinear physically parameterized model are identified directly in the time domain. The procedure is exemplified using real data from an experimental industrial robot.

  • 23.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nonlinear Identification of a Physically Parameterized Robot Model2006Report (Other academic)
    Abstract [en]

    In the work presented here, a three-step identification procedure for rigid body dynamics, friction, and flexibilities, introduced in (Wernholt and Gunnarsson, 2005), will be utilized and extended. Using the procedure, the parameters can be identified only using motor measurements. In the first step, rigid body dynamics and friction will be identified using a separable least squares method, where a friction model describing the Striebeck effect is used. In the second step, initial values for flexibilities are obtained using inverse eigenvalue theory. Finally, in the last step, the remaining parameters of a nonlinear physically parameterized model are identified directly in the time domain. The procedure is exemplified using real data from an experimental industrial robot.

  • 24.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On the Use of a Multivariable Frequency Response Estimation Method for Closed Loop Identification2004Report (Other academic)
    Abstract [en]

    A method for estimating the Multivariable Frequency Response Function using closed loop data is studied. An approximate expression for the estimation error is derived, and using this expression some properties of the estimation error can be explained. Of particular interest is how the model quality is affected by the properties of the disturbances, the choice of excitation signal in the different input channels, the feedback and the properties of the system itself. The expression is illustrated by simulation data from an industrial robot.

  • 25.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    On the Use of a Multivariable Frequency Response Function Estimation Method for Closed Loop Identification2004In: Proceedings of the 43rd IEEE Conference on Decision and Control, 2004, p. 827-832Conference paper (Refereed)
    Abstract [en]

    A method for estimating the Multivariable Frequency Response Function using closed loop data is studied. An approximate expression for the estimation error is derived, and using this expression some properties of the estimation error can be explained. Of particular interest is how the model quality is affected by the properties of the disturbances, the choice of excitation signal in the different input channels, the feedback and the properties of the system itself. The expression is illustrated by simulation data from an industrial robot.

  • 26.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Gunnarsson, Svante
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hanssen, Sven
    ABB Automation Technologies, Sweden.
    Moberg, Stig
    ABB Automation Technologies, Sweden.
    On the Use of a Multivariable Frequency Response Identification Method in the Presence of Periodic Disturbances2003Report (Other academic)
    Abstract [en]

    A method for estimating the Multivariable Frequency Response Function (MFRF) is evaluated with respect to different disturbance properties and excitation signals. For the robot application, disturbances are mainly of a periodic nature, and Gaussian disturbance descriptions, which are often used in identification literature, do not give the same result. It is shown that the averaging technique over multiple periods does not work as well for periodic disturbances as for Gaussian disturbances. The chirp excitation signal might be a better choice than multisine to reduce the influence of periodic disturbances. The main reason for this is a varying operating point.

  • 27.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Löfberg, Johan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Experiment Design for Identification of Nonlinear Gray-box Models with Application to Industrial Robots2007Report (Other academic)
    Abstract [en]

    Experiment design involving selection of optimal experiment positions for nonlinear gray-box models is studied. From the derived Fisher information matrix, a convex optimization problem is posed. By considering the dual problem, the experiment design is efficiently solved with linear complexity in the number of candidate positions, compared to cubic complexity for the primal problem. In the numerical illustration, using an industrial robot, the parameter covariance is reduced by a factor of six by using the 15 optimal positions compared to using the optimal single position in all experiments.

  • 28.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Löfberg, Johan
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Experiment Design for Identification of Nonlinear Gray-Box Models with Application to Industrial Robots2007In: Proceedings of the 46th IEEE Conference on Decision and Control, 2007, p. 5110-5116Conference paper (Refereed)
    Abstract [en]

    Experiment design involving selection of optimal experiment positions for nonlinear gray-box models is studied. From the derived Fisher information matrix, a convex optimization problem is posed. By considering the dual problem, the experiment design is efficiently solved with linear complexity in the number of candidate positions, compared to cubic complexity for the primal problem. In the numerical illustration, using an industrial robot, the parameter covariance is reduced by a factor of six by using the 15 optimal positions compared to using the optimal single position in all experiments.

  • 29.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Moberg, Stig
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Experimental Comparison of Methods for Multivariable Frequency Response Function Estimation2007Report (Other academic)
    Abstract [en]

    Nonparametric estimation methods for the multivariable frequency response function are experimentally evaluated using closed-loop data from an industrial robot. Three classical estimators (H1, joint input-output, arithmetic mean) and two estimators based on nonlinear averaging techniques (harmonic mean, geometric/logarithmic mean) are considered. The estimators based on nonlinear averaging give the best results, followed by the arithmetic mean estimator, which gives a slightly larger bias. The joint input-output estimator, which is asymptotically unbiased in theory, turns out to give large bias errors for low frequencies. Finally, the H1 estimator gives the largest bias for all frequencies.

  • 30.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Moberg, Stig
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Experimental Comparison of Methods for Multivariable Frequency Response Function Estimation2008In: Proceedings of the 17th IFAC World Congress, 2008, p. 15359-15366Conference paper (Refereed)
    Abstract [en]

    Nonparametric estimation methods for the multivariable frequency response function are experimentally evaluated using closed-loop data from an industrial robot. Three classical estimators (H1, joint input-output, arithmetic mean) and two estimators based on nonlinear averaging techniques (harmonic mean, geometric/logarithmic mean) are considered. The estimators based on nonlinear averaging give the best results, followed by the arithmetic mean estimator, which gives a slightly larger bias. The joint input-output estimator, which is asymptotically unbiased in theory, turns out to give large bias errors for low frequencies. Finally, the H1 estimator gives the largest bias for all frequencies.

  • 31.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Moberg, Stig
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Frequency-Domain Gray-Box Identification of Industrial Robots2007Report (Other academic)
    Abstract [en]

    This paper considers identification of unknown parameters in elastic dynamic models of industrial robots. Identifying such models is a challenging task since an industrial robot is a multivariable, nonlinear, resonant, and unstable system. Unknown parameters (mainly spring-damper pairs) in a physically parameterized nonlinear dynamic model are identified in the frequency domain, using estimates of the nonparametric frequency response function (FRF) in different robot configurations/positions. The nonlinear parametric robot model is linearized in the same positions and the optimal parameters are obtained by minimizing the discrepancy between the nonparametric FRFs and the parametric FRFs (the FRFs of the linearized parametric robot model). In order to accurately estimate the nonparametric FRFs, the experiments must be carefully designed. The selection of optimal robot configurations for the experiments is also part of the design. Different parameter estimators are compared and experimental results show the usefulness of the proposed identification procedure. The weighted logarithmic least squares estimator achieves the best result and the identified model gives a good global description of the dynamics in the frequency range of interest.

  • 32.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Moberg, Stig
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Frequency-Domain Gray-Box Identification of Industrial Robots2008In: Proceedings of the 17th IFAC World Congress, 2008, p. 15372-15380Conference paper (Refereed)
    Abstract [en]

    This paper considers identification of unknown parameters in elastic dynamic models of industrial robots. Identifying such models is a challenging task since an industrial robot is a multivariable, nonlinear, resonant, and unstable system. Unknown parameters (mainly spring-damper pairs) in a physically parameterized nonlinear dynamic model are identified in the frequency domain, using estimates of the nonparametric frequency response function (FRF) in different robot configurations/positions. The nonlinear parametric robot model is linearized in the same positions and the optimal parameters are obtained by minimizing the discrepancy between the nonparametric FRFs and the parametric FRFs (the FRFs of the linearized parametric robot model). In order to accurately estimate the nonparametric FRFs, the experiments must be carefully designed. The selection of optimal robot configurations for the experiments is also part of the design. Different parameter estimators are compared and experimental results show the usefulness of the proposed identification procedure. The weighted logarithmic least squares estimator achieves the best result and the identified model gives a good global description of the dynamics in the frequency range of interest.

  • 33.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Moberg, Stig
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Nonlinear Gray-Box Identification Using Local Models Applied to Industrial Robots2011In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 47, no 4, p. 650-660Article in journal (Refereed)
    Abstract [en]

    In this paper, we study the problem of estimating unknown parameters in nonlinear gray-box models that may be multivariable, nonlinear, unstable, and resonant at the same time. A straightforward use of time-domain predication-error methods for this type of problem easily ends up in a large and numerically stiff optimization problem. We therefore propose an identification procedure that uses intermediate local models that allow for data compression and a less complex optimization problem. The procedure is based on the estimation of the nonparametric frequency response function (FRF) in a number of operating points. The nonlinear gray-box model is linearized in the same operating points, resulting in parametric FRFs. The optimal parameters are finally obtained by minimizing the discrepancy between the nonparametric and parametric FRFs. The procedure is illustrated by estimating elasticity parameters in a six-axes industrial robot. Different parameter estimators are compared and experimental results show the usefulness of the proposed identification procedure. The weighted logarithmic least squares estimator achieves the best result and the identified model gives a good global description of the dynamics in the frequency range of interest for robot control.

  • 34.
    Wernholt, Erik
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Östring, Måns
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Modeling and Control of a Bending Backwards Industrial Robot2003Report (Other academic)
    Abstract [en]

    In this work we have looked at various parts of modeling of robots. First the rigid body motion is studied, spanning from kinematics to dynamics and path and trajectory generation. We have also looked into how to extend the rigid body model with flexible gear-boxes and how this could be incorporated with Robotics Toolbox. A very simple feedforward control based on the rigid model is applied in addition to PID control and in the simulations the overshoot is halved compared to only PID control. We have also tried LQ control and in our simulations the effect of torque disturbances on the arm has been lowered by a factor five, compared to diagonal PID controllers.

  • 35.
    Öhr, Jonas
    et al.
    ABB, Sweden.
    Moberg, Stig
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wernholt, Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hanssen, Sven
    ABB, Sweden.
    Pettersson, Jens
    ABB, Sweden.
    Persson, Sofie
    ABB, Sweden.
    Sander-Tavallaey, Shiva
    ABB, Sweden.
    Identification of Flexibility Parameters of 6-axis Industrial Manipulator Models2006In: Proceedings of the 2006 International Conference on Noise and Vibration Engineering, 2006, p. 3305-Conference paper (Refereed)
    Abstract [en]

    A method for identification of flexibility parameters of a 18 DOF (degrees offreedom) robot prototype model is proposed. Experiments show the strengthof the method and the results indicates that flexibilities in the bearings and thearms, taken together, are of the same order as the flexibilities in the gears.

  • 36.
    Öhr, Jonas
    et al.
    ABB, Sweden.
    Moberg, Stig
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Wernholt, Erik
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Hanssen, Sven
    ABB, Sweden.
    Pettersson, Jens
    ABB, Sweden.
    Persson, Sofie
    ABB, Sweden.
    Sander-Tavallaey, Shiva
    ABB, Sweden.
    Identification of Flexibility Parameters of 6-axis Industrial Manipulator Models2006Report (Other academic)
    Abstract [en]

    A method for identification of flexibility parameters of a 18 DOF (degrees offreedom) robot prototype model is proposed. Experiments show the strengthof the method and the results indicates that flexibilities in the bearings and thearms, taken together, are of the same order as the flexibilities in the gears.

1 - 36 of 36
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