Modeling, identification, and control of a pneumatically actuated, force controllable robot

Bobrow, J.E.; McDonell, B.W.

doi:10.1109/70.720349

Cited by 160 publications

(77 citation statements)

References 21 publications

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“…The advantage of having extra control of individual pressures is that we can prevent undesirable pressure buildup, which causes increased seal friction in the actuator and a reduced passive compliance, leading to a poorer performance while in backdrive mode. We refer the reader to [18], [21], and [22] for more details.…”

Section: B Force Control Algorithmmentioning

confidence: 99%

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A Robot and Control Algorithm That Can Synchronously Assist in Naturalistic Motion During Body-Weight-Supported Gait Training Following Neurologic Injury

Aoyagi

Ichinose

Harkema

et al. 2007

IEEE Trans. Neural Syst. Rehabil. Eng.

227

143

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Abstract-Locomotor training using body weight support on a treadmill and manual assistance is a promising rehabilitation technique following neurological injuries, such as spinal cord injury (SCI) and stroke. Previous robots that automate this technique impose constraints on naturalistic walking due to their kinematic structure, and are typically operated in a stiff mode, limiting the ability of the patient or human trainer to influence the stepping pattern. We developed a pneumatic gait training robot that allows for a full range of natural motion of the legs and pelvis during treadmill walking, and provides compliant assistance. However, we observed an unexpected consequence of the device's compliance: unimpaired and SCI individuals invariably began walking out-ofphase with the device. Thus, the robot perturbed rather than assisted stepping. To address this problem, we developed a novel algorithm that synchronizes the device in real-time to the actual motion of the individual by sensing the state error and adjusting the replay timing to reduce this error. This paper describes data from experiments with individuals with SCI that demonstrate the effectiveness of the synchronization algorithm, and the potential of the device for relieving the trainers of strenuous work while maintaining naturalistic stepping.

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Section: B Force Control Algorithmmentioning

confidence: 99%

“…To achieve position control, we adopt the hierarchical control strategy that was successfully applied to pneumatic actuators by McDonell [22]. The force-tracking controller acts as the inner loop, and the position controller as the outer loop.…”

Section: Position Control Algorithmmentioning

confidence: 99%

A Robot and Control Algorithm That Can Synchronously Assist in Naturalistic Motion During Body-Weight-Supported Gait Training Following Neurologic Injury

Aoyagi

Ichinose

Harkema

et al. 2007

IEEE Trans. Neural Syst. Rehabil. Eng.

227

143

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“…To control the pneumatic cylinders in the POGO and PAM, we model and cancel the nonlinear compressible air flow dynamics for each cylinder and servomechanism valve and use pressure sensors on both sides of the pistons for force feedback [37,39]. This technique allows us to achieve good force or position control.…”

Section: Control Strategiesmentioning

confidence: 99%

Tools for understanding and optimizing robotic gait training

Reinkensmeyer

Aoyagi²,

Emken³

et al. 2006

JRRD

132

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Abstract-This article reviews several tools we have developed to improve the understanding of locomotor training following spinal cord injury (SCI), with a view toward implementing locomotor training with robotic devices. We have developed (1) a small-scale robotic device that allows testing of locomotor training techniques in rodent models, (2) an instrumentation system that measures the forces and motions used by experienced human therapists as they manually assist leg movement during locomotor training, (3) a powerful, lightweight leg robot that allows investigation of motor adaptation during stepping in response to force-field perturbations, and (4) computational models for locomotor training. Results from the initial use of these tools suggest that an optimal gait-training robot will minimize disruptive sensory input, facilitate appropriate sensory input and gait mechanics, and intelligently grade and time its assistance. Currently, we are developing a pneumatic robot designed to meet these specifications as it assists leg and pelvic motion of people with SCI.

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“…When these are combined for use in a multi-DOF robot, it is, however, extremely hard to model the dynamics analytically for accurate motion control. In fact, related studies, using well-established methods from control theory, had to resort to limiting the number of DOF of the systems being modeled, and/or address the the problem only in the context of static tasks like position control [3,21,2].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Inverse Dynamics Learning for Musculoskeletal Robots based on Echo State Gaussian Process Regression

Hartmann¹,

Boedecker²,

Obst³

et al. 2012

Robotics: Science and Systems VIII

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Abstract-A challenging topic in articulated robots is the control of redundantly many degrees of freedom with artificial muscles. Actuation with these devices is difficult to solve because of nonlinearities, delays and unknown parameters such as friction. Machine learning methods can be used to learn control of these systems, but are faced with the additional problem that the size of the search space prohibits full exploration in reasonable time. We propose a novel method that is able to learn control of redundant robot arms with artificial muscles online from scratch using only the position of the end effector, without using any joint positions, accelerations or an analytical model of the system or the environment. To learn in real time, we use the so called online "goal babbling" method to effectively reduce the search space, a recurrent neural network to represent the state of the robot arm, and novel online Gaussian processes for regression. With our approach, we achieve good performance on trajectory tracking tasks for the end effector of two very challenging systems: a simulated 6 DOF redundant arm with artificial muscles, and a 7 DOF robot arm with McKibben pneumatic artificial muscles. We also show that the combination of techniques we propose results in significantly improved performance over using the individual techniques alone.

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Modeling, identification, and control of a pneumatically actuated, force controllable robot

Cited by 160 publications

References 21 publications

A Robot and Control Algorithm That Can Synchronously Assist in Naturalistic Motion During Body-Weight-Supported Gait Training Following Neurologic Injury

A Robot and Control Algorithm That Can Synchronously Assist in Naturalistic Motion During Body-Weight-Supported Gait Training Following Neurologic Injury

Tools for understanding and optimizing robotic gait training

Real-Time Inverse Dynamics Learning for Musculoskeletal Robots based on Echo State Gaussian Process Regression

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