Modeling and control of a Pneumatic-Electric hybrid system

Teramae, Tatsuya; Noda, Tomoyuki; Hyon, Sang-Ho; Morimoto, Jun

doi:10.1109/iros.2013.6697061

Cited by 10 publications

(23 citation statements)

References 10 publications

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“…The one joint robot is composed of an antagonistically organized Festo MAS-40 pneumatic artificial muscle (PAM) pair (see Figure 1 ) (Noda et al, 2013 ; Teramae et al, 2013 ). Each PAM is connected to a rotational disk/pulley system by string tendons housing an arm of 35 cm.…”

Section: Methodsmentioning

confidence: 99%

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Human-In-The-Loop Control and Task Learning for Pneumatically Actuated Muscle Based Robots

Teramae¹,

Ishihara²,

Babič

et al. 2018

Front. Neurorobot.

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Pneumatically actuated muscles (PAMs) provide a low cost, lightweight, and high power-to-weight ratio solution for many robotic applications. In addition, the antagonist pair configuration for robotic arms make it open to biologically inspired control approaches. In spite of these advantages, they have not been widely adopted in human-in-the-loop control and learning applications. In this study, we propose a biologically inspired multimodal human-in-the-loop control system for driving a one degree-of-freedom robot, and realize the task of hammering a nail into a wood block under human control. We analyze the human sensorimotor learning in this system through a set of experiments, and show that effective autonomous hammering skill can be readily obtained through the developed human-robot interface. The results indicate that a human-in-the-loop learning setup with anthropomorphically valid multi-modal human-robot interface leads to fast learning, thus can be used to effectively derive autonomous robot skills for ballistic motor tasks that require modulation of impedance.

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Section: Methodsmentioning

confidence: 99%

“…τ u and τ l are torques generated by the upper and lower PAMs, respectively. The torque was calculated with a model of a PAM actuator as in Teramae et al ( 2013 ). We convert the continuous time robot dynamics Equation (7) to a discrete time model to formulate the optimal control problem described in Equation (4).…”

Section: Methodsmentioning

confidence: 99%

Human-In-The-Loop Control and Task Learning for Pneumatically Actuated Muscle Based Robots

Teramae¹,

Ishihara²,

Babič

et al. 2018

Front. Neurorobot.

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“…This paper mainly considers τ P AM because controlling τ motor usually has an easier solution in motor driver. As we developed in our previous studies [14]- [17], τ P AM covers a large torque for gravity compensation for low frequency torque generation. The motor covers high frequency torque but it can be small with low reduction gear that is aimed to possess backdrivability.…”

Section: Concept and Design Policymentioning

confidence: 99%

“…As the actuator system, we use pneumatic-electric hybrid actuators that we have been developing in our previous studies [14]- [17] but with PAMs force/displacement transmitted via Bowden cables in this paper. Pneumatic-Electric Hybrid Actuator (PEHA) is composed of a pair of Pneumatic Artificial Muscles (PAMs) and a small-size electromagnetic motor, where the PAMs can The authors are with Dept.…”

Section: Introductionmentioning

confidence: 99%

Development of an upper limb exoskeleton powered via pneumatic electric hybrid actuators with bowden cable

Noda¹,

Teramae²,

Uğurlu

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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In this paper, we introduce our ongoing work on the development of an upper body exoskeleton robot, driven by a pneumatic-electric hybrid actuation system. Since the limb of an exoskeleton robot needs to have small inertia to achieve agility and safety, using a heavy actuator is not preferable. Furthermore, we need to use backdrivable actuators that can generate sufficiently large torques to support user movements. These two requirements may seem contradictory. In order to cope with this development problem, we use a hybrid actuation system composed of Pneumatic Artificial Muscles (PAMs) and small-size electromagnetic motors. Although we and other research groups have already presented the advantage of the hybrid actuation system, we newly propose the usage of Bowden cable in a hybrid actuator to transmit the force generated by the PAMs to joints of our exoskeleton robot so that we can design a compact upper limb with small inertia. In addition, small size electric motors are mechanically connected to joints in order to compensate uncertainty generated by the PAM dynamics and the Bowden cable. We demonstrate that the proposed joint is backdrivable with the capability of large torque generation for the gravity compensation task both in One-DOF system with a dummy weight and right arm of the upper body exoskeleton with a mannequin arm. We also show the right arm exoskeleton can be moved using a torque input, extracted from sensory information via a goniometer.978-1-4799-6934-0/14/$31.00 ©2014 IEEE

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“…Cao et al (2018);Merola et al (2018);Park et al (2014) used linear dynamical models Teramae et al (2018). used a static PAM model proposed for position control and employed a PAM model identification method proposed byTeramae et al (2013) Ugurlu et al (2015). proposed the use of nonlinear dynamical PAM models and applied them to the control of a lower-limb exoskeleton robotUgurlu et al (2016) Koller et al (2016),Peternel et al (2016a),.…”

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confidence: 99%

Design of physical user–robot interactions for model identification of soft actuators on exoskeleton robots

Hamaya

Matsubara

Teramae³

et al. 2019

The International Journal of Robotics Research

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Recent breakthroughs in wearable robots, such as exoskeleton robots with soft actuators and soft exosuits, have enabled the use of safe and comfortable movement assistance. However, modeling and identification methods for soft actuators used in wearable robots have yet to be sufficiently explored. In this study, we propose a novel approach for obtaining accurate soft actuator models through the design of physical user-robot interactions for wearable robots, in which the user applies external forces to the robot. To obtain an accurate soft actuator model from the limited amount of data acquired through an interaction, we leverage an active learning framework based on Gaussian process regression. We conducted experiments using a two-degree-of-freedom upper-limb exoskeleton robot with four pneumatic artificial muscles (PAMs). Experimental results showed that physical interactions between the exoskeleton robot and the user were successfully designed to allow PAM models to be identified. Furthermore, we found that data acquired through an interaction could result in more accurate soft actuator models for the exoskeleton robots than data acquired without a physical interaction between the exoskeleton robot and the user.

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Modeling and control of a Pneumatic-Electric hybrid system

Cited by 10 publications

References 10 publications

Human-In-The-Loop Control and Task Learning for Pneumatically Actuated Muscle Based Robots

Human-In-The-Loop Control and Task Learning for Pneumatically Actuated Muscle Based Robots

Development of an upper limb exoskeleton powered via pneumatic electric hybrid actuators with bowden cable

Design of physical user–robot interactions for model identification of soft actuators on exoskeleton robots

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