2018 IEEE Aerospace Conference 2018
DOI: 10.1109/aero.2018.8396710
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Force sensorless impedance control for a space robot to capture a satellite for on-orbit servicing

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Cited by 18 publications
(12 citation statements)
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“…The angular velocity can be directly measured using a gyro and the joint angles are measured by the encoders. On the other hand, the linear velocity, required only by the observer (5), and the joint velocities, required by both observers, are not directly measured but need to be reconstructed, either by using a discrete derivative or by fusion. Herein the problem of the reconstruction is not addressed and a simplified noise performance model has been used for the sake of comparing the performance of the two observer schemes with similar noise conditions.…”
Section: B Measurement Modelmentioning
confidence: 99%
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“…The angular velocity can be directly measured using a gyro and the joint angles are measured by the encoders. On the other hand, the linear velocity, required only by the observer (5), and the joint velocities, required by both observers, are not directly measured but need to be reconstructed, either by using a discrete derivative or by fusion. Herein the problem of the reconstruction is not addressed and a simplified noise performance model has been used for the sake of comparing the performance of the two observer schemes with similar noise conditions.…”
Section: B Measurement Modelmentioning
confidence: 99%
“…For this reason, the space robot can be equipped with a force-torque sensor duly placed at the wrist. However, if the contact does not occur exactly at the foreseen location, the measurement may be inaccurate [5]. Furthermore, this kind of sensor can not be redundant and thus a failure could jeopardize the successful accomplishment of the task.…”
Section: Introductionmentioning
confidence: 99%
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“…In addition, the uncertainty of the end-effector contact force is also one of the factors affecting the control results [ 25 ]. Duan J et al [ 26 ] assumed that the contact process was like the compression of a linear spring and identified the stiffness coefficient [ 27 , 28 ] by adaptive algorithms [ 29 , 30 ]. Xue C et al [ 31 ] compensated the uncertainties in robotic manipulator dynamics through the radial basis functions neural networks (RBFNNs).…”
Section: Introductionmentioning
confidence: 99%
“…For example, an impedance controller is proposed in [2] to grasp a tumbling target. In addition, it has been proven that this kind of control also works without the need of sensor data (force feedback), using a disturbance observer, also in space grasping [3]. When robotic system has more than one arm an IC variation exists called multiple impedance control (MIC), which was also proven to grasp [4].…”
Section: Introductionmentioning
confidence: 99%