Extension of impedance matching to nonlinear dynamics of robotic tasks

Arimoto, Suguru; Han, Hyun-Yong; Cheah, Chien Chern; Kawamura, Sadao

doi:10.1016/s0167-6911(98)00084-x

Cited by 34 publications

(15 citation statements)

References 11 publications

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“…Extensions of this passivity argument for a learning control problem of physical interaction between a finger-tip covered by soft and deformable material and a rigid object arising in robotic tasks have been reported in recent papers [39][40][41] . A further extended discussion of equivalence among learnability, output-dissipativity, and strictly positive realness with an extra condition is found in our next paper 42 .…”

Section: Discussionmentioning

confidence: 91%

Learnability and Adaptability from the Viewpoint of Passivity Analysis

Arimoto

Naniwa²

2002

Intelligent Automation & Soft Computing

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This paper attempts to give a mathematical and physical interpretation of practicebased learning (so-called 'learning control') from the passivity viewpoint for a class of linear dynamical systems with passivity and general nonlinear differential equations of robotic motion. It is shown from an axiomatic argument that the passivity of a pair of input and output plays a crucial role in the ability of learning. More precisely in case of robot dynamics it is shown that the passivity between a residual input torque vector and an output as a linear combination of the angular velocity error and the saturated joint angle error enables trajectory tracking errors of the robot motion to converge to zero with repeating practices. For a class of robot tasks when the endpoint is holonomically constrained on a surface, the problem of convergence of residual position and force trajectory tracking errors is also discussed under the joint-space orthogonalization principle.

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Section: Discussionmentioning

confidence: 91%

Learnability and Adaptability from the Viewpoint of Passivity Analysis

Arimoto

Naniwa²

2002

Intelligent Automation & Soft Computing

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“…It is easy to show that according to Equation (34) together with Equations (36) and (37) the pair +! f I , y I , satisfy passivity similarly to the discussion in the case that f is constant (see Reference [9]). At the same time both the pairs + f I , x I , and + f I , F I , satis"es passivity and hence they are a conjugate power pair.…”

Section: Learning Of Force Control Via Impedance Matchingmentioning

confidence: 85%

“…mechanical "nger with soft "nger-tip presses a rigid object. setpoint force control (see Reference [9]), the speed of convergence becomes slower than that in the case of the single d.o.f. system shown in Figure 10.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Iterative learning of impedance control from the viewpoint of passivity

Arimoto

Han

Nguyen

et al. 2000

Int. J. Robust Nonlinear Control

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This paper proposes an iterative learning control scheme for impedance control of robotic tasks when the tool endpoint covered by soft and deformable material presses a rigid object or environment at a prescribed periodic force pattern. To this end, an iterative learning control scheme for a class of linear dynamical systems with a negative feedback structure is analysed and convergence of the proposed learning update law after a sufficient number of repetitions is proved. It is shown that this convergence realizes impedance matching in a sense of electric circuit theory if the feedback system can be expressed as a lumped‐parameter electric circuit. The iterative learning control scheme is then applied for a case of impedance control of robotic tasks when the characteristics of reproducing force of the deformable material is nonlinear in its displacement and unknown and the tool mass is uncertain. Simulation results are also presented, which show effectiveness of the proposed learning control scheme. Copyright © 2000 John Wiley & Sons, Ltd.

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“…To this aim, compliant force regulation has attracted the attention of many researchers (e.g. ). From a safety perspective of contact tasks, it is preferable that the robot is guaranteed to interact with the deformable environment in an energetically passive manner.…”

Section: Introductionmentioning

confidence: 99%

A force regulation guaranteeing input‐to‐state stability for a robot manipulator in a potential field

Satoh

Ebimoto

Saeki

2017

IEEJ Transactions Elec Engng

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This paper is concerned with the compliant force regulation for a robot manipulator interacting with a deformable environment described as a potential field. We extend the conventional result of the asymptotic force regulation based on the energy shaping method for a port-Hamiltonian system. Additionally, we add an extra compensator and equip a modified integrator dynamics to the proposed controller in order to introduce sufficient degrees of freedom. Those degrees of freedom enable dealing with a wider class of disturbances, which are possibly time-varying and appear in the dynamics of all the variables of the closedloop system. Moreover, first, we prove that the proposed controller achieves asymptotic force regulation at the desired position without disturbances. Second, we prove that the closed-loop system with the proposed controller becomes input-to-state stable with respect to any bounded disturbances and guarantees that both the solution of the system and resultant interaction force remain bounded.

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Extension of impedance matching to nonlinear dynamics of robotic tasks

Cited by 34 publications

References 11 publications

Learnability and Adaptability from the Viewpoint of Passivity Analysis

Learnability and Adaptability from the Viewpoint of Passivity Analysis

Iterative learning of impedance control from the viewpoint of passivity

A force regulation guaranteeing input‐to‐state stability for a robot manipulator in a potential field

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