Impedance Control: An Approach to Manipulation: Part II—Implementation

By Neville Hogan. 1985

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Citations in the corpus

Learning from Physical Human Corrections, One Feature at a Time (2018, background): “Traditionally, the human’s interactions are treated in one of three ways [9]: as disturbances to be rejected [5, 12, 23], as collisions to be detected and avoided [4], or as operator signals to be followed by switching into a compliant mode [8, 10, 14].”
ARMin: a robot for patient-cooperative arm therapy (2007, background): “Back-driveability is required for good performance of the impedance control [10–12] and it is advantageous for the safety of exoskeleton robots (cf.”
Force tracking impedance control of robot manipulators under unknown environment (2004, background): “After the pioneering work of the impedance control by Hogan [5] there have been several major research issues to be solved on the impedance control.”
Adaptive Impedance Control for an Upper Limb Robotic Exoskeleton Using Biological Signals (2017, background): “Motivated by the previous works [7], [18], impedance control has been extensively investigated, which adjusts the mechanical impedance specified by a target model.”
Control strategies for active lower extremity prosthetics and orthotics: a review (2015, background): “A leading theory regarding physiological movement is that the CNS controls th e limbs through impedance control [204,205].”
Human–robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017 (2019, background): “For such schemes, the human safety is assured by limiting the maximum energy of the impact [50], thanks to the robot compliant structure or by using the measured force in the control scheme [313,314].”
Learning variable impedance control (2011, methodology): “Hogan (1985b) discussed a variety of impedance control schemes.”
Proprioceptive Actuator Design in the MIT Cheetah: Impact Mitigation and High-Bandwidth Physical Interaction for Dynamic Legged Robots (2017, background): “effects are much more difficult to shape through closed-loop impedance control [16], [28].”
Learning Robot Objectives from Physical Human Interaction (2017, methodology): “Several reactive control strategies have been developed to deal with pHRI [1, 2, 3].”