Novel equilibrium-point control of agonist-antagonist system with pneumatic artificial muscles

Ariga, Yohei; Pham, Hang; Uemura, Mamoru; Hirai, Hiroaki; Miyazaki, Fumio

doi:10.1109/icra.2012.6224662

Cited by 28 publications

(18 citation statements)

References 8 publications

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“…Here, we addressed the four pairs of antagonistic muscles that mainly contributed to the studied task. The relationship between the A-A ratio and the EP also supports the physical meaning of the proposed A-A ratio (Ariga et al 2012a). When m 1 increases and m 2 decreases, the A-A ratio r 1 increases.…”

Section: (3) A-a Ratio and A-a Sum Datasetssupporting

confidence: 74%

“…When m 1 increases and m 2 decreases, the A-A ratio r 1 increases. This relationship between the A-A sum and joint stiffness was experimentally proved in Ariga et al (2012a) by using the antagonistic system with PAMs. Hence, a change in the A-A ratio r 1 will influence the kinematics of shoulder-joint movement.…”

Section: (3) A-a Ratio and A-a Sum Datasetsmentioning

confidence: 82%

“…Muscle synergies transform the desired control of task variables into high-dimensional muscle activations to produce biomechanical outputs that generate sensory signals mapping onto task variables. The A-A sum for a PAM system, defined as the sum of the air pressure of the extensor and flexor artificial muscles, is directly and linearly associated with its mechanical stiffness at any EP (Ariga et al 2012a). However, the redundant degrees-of-freedom problem also exists in the transformation between muscle activation patterns and kinematic patterns of movement.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cognitive Neuroscience Robotics A

Kasaki¹,

Ishiguro²,

Asada³

et al. 2016

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Section: (3) A-a Ratio and A-a Sum Datasetssupporting

confidence: 74%

Section: (3) A-a Ratio and A-a Sum Datasetsmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cognitive Neuroscience Robotics A

Kasaki¹,

Ishiguro²,

Asada³

et al. 2016

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“…Numerous authors have already presented technical realization of humanlike actuation in cutting edge robotics. At Osaka University antagonistically coupled pneumatic actuator is described in [10]. In German Aerospace Center (DLR) tendon driven antagonistic robotic actuator is demonstrated in [11].…”

Section: Fig 1 -Artificial Muscles: A-mckibben Pneumatic Actuator Bmentioning

confidence: 99%

Hill’s and Huxley’s muscle models - tools for simulations in biomechanics

Jovanović

Vranic

Miljković³

2015

Serb J Electr Eng

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Numerous mathematical models of human skeletal muscles have been developed. However, none of them is adopted as a general one and each of them is suggested for some specific purpose. This topic is essential in humanoid robotics, since we firstly need to understand how human moves and acts in order to exploit human movement patterns in robotics and design human like actuators. Simulations in biomechanics are intensively used in research of locomotion, safe human-robot interaction, development of novel robotic actuators, biologically inspired control algorithms, etc. This paper presents two widely adopted muscle models (Hill's and Huxley's model), elaborates their features and demonstrates trade-off between their accuracy and efficiency of computer simulations. The simulation setup contains mathematical representation of passive muscle structures as well as mathematical model of an elastic tendon as a series elastic actuation element. Advanced robot control techniques point out energy consumption as one of the key issues. Therefore, energy store and release mechanism in elastic elements in both tendon and muscle, based on the simulation models, are considered.

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“…In a single-joint robot arm control, Ariga et al (2012) introduced the concepts of the agonist-antagonist muscle-pairs ratio and the agonist-antagonist muscle-pairs activity, and developed PM actuated joint angle and joint stiffness equilibrium-point control. Joint stiffness and position control may become one of the central research themes of PM research in the future.…”

Section: Introductionmentioning

confidence: 99%

Modelling of pneumatic muscle actuator and antagonistic joint using linearised parameters

Sui

Xie

2013

IJBBR

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This paper proposes a modelling approach using two general parameters of pneumatic muscle actuator based on a linearised method. After the analysis of the two linearised parameters, which are stiffness and pressure-force coefficient, a model of pneumatic muscle is developed based on assumptions of the linearised parameters. Joint stiffness and pressure-torque coefficient are proposed and applied to the analysis of an antagonistic joint actuated by pneumatic muscles. A joint model is obtained using the linearised parameters, and a position and stiffness control model of the joint is developed.

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Novel equilibrium-point control of agonist-antagonist system with pneumatic artificial muscles

Cited by 28 publications

References 8 publications

Cognitive Neuroscience Robotics A

Cognitive Neuroscience Robotics A

Hill’s and Huxley’s muscle models - tools for simulations in biomechanics

Modelling of pneumatic muscle actuator and antagonistic joint using linearised parameters

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