Friction and rigid body identification of robot dynamics

Grotjahn, Martin; Daemi, Mehdi; Heimann, Bodo

doi:10.1016/s0020-7683(00)00141-4

Cited by 98 publications

(64 citation statements)

References 18 publications

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“…In Grotjahn et al (2001), the authors describe an identification method for industrial robots that does not require the a priori identification of the friction model, which is one of the more difficult tasks. The proposed method relies on weighted least squares (WLS) with the advantage of it being relative simplicity of implementation for standard robot controls and the possibility it provides in identifying unmodelled effects and unnecessary parameters.…”

Section: Model Parameter Estimationmentioning

confidence: 99%

Improvement of a Robotic Manipulator Model Based on Multivariate Residual Modeling

Gale¹,

Rahmati²,

Gravdahl³

et al. 2017

Front. Robot. AI

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A new method is presented for extending a dynamic model of a six degrees of freedom robotic manipulator. A non-linear multivariate calibration of input-output training data from several typical motion trajectories is carried out with the aim of predicting the model systematic output error at time (t + 1) from known input reference up till and including time (t). A new partial least squares regression (PLSR) based method, nominal PLSR with interactions was developed and used to handle, unmodelled non-linearities. The performance of the new method is compared with least squares (LS). Different crossvalidation schemes were compared in order to assess the sampling of the state space based on conventional trajectories. The method developed in the paper can be used as fault monitoring mechanism and early warning system for sensor failure. The results show that the suggested methods improves trajectory tracking performance of the robotic manipulator by extending the initial dynamic model of the manipulator.

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Section: Model Parameter Estimationmentioning

confidence: 99%

Improvement of a Robotic Manipulator Model Based on Multivariate Residual Modeling

Gale¹,

Rahmati²,

Gravdahl³

et al. 2017

Front. Robot. AI

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show abstract

“…In the identification process different nonlinear friction models have been used. For example the following models (Grotjahn et al, 2001), proposed (Farhat, 2006) for friction modeling has the form,…”

Section: Friction Modelsmentioning

confidence: 99%

Dynamic Parameter Identification for Parallel Manipulators

Mata¹,

Farhat²,

Díaz‐Rodríguez³

et al. 2008

Parallel Manipulators, Towards New Applications

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“…There exists a vast amount of literature on the identification of rigid body dynamics, see, for example, (Grotjahn et al, 2001;Gautier and Poignet, 2001;Kozlowski, 1998;Swevers et al, 1997). The standard procedure includes a dynamic model…”

Section: Step 1: Rigid Body Dynamics and Frictionmentioning

confidence: 99%

Nonlinear Identification of a Physically Parameterized Robot Model 1

Wernholt

Gunnarsson

2006

IFAC Proceedings Volumes

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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. Erik Wernholt and Svante Gunnarsson Dept. of Electrical Engineering, Linköping University, SE-581 83 Linköping, SwedenAbstract: 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.

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Friction and rigid body identification of robot dynamics

Cited by 98 publications

References 18 publications

Improvement of a Robotic Manipulator Model Based on Multivariate Residual Modeling

Improvement of a Robotic Manipulator Model Based on Multivariate Residual Modeling

Dynamic Parameter Identification for Parallel Manipulators

Nonlinear Identification of a Physically Parameterized Robot Model 1

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