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[Th017] Citation: Abstract
The goals of this research are: 1) to apply knowledge of human
neuro-musculo-skeletal motion control to build a physiology based
neural controller and biomechanics based "anthroform" robotic arm
system, and 2) to utilize the neural controller and anthroform arm
system to study some controversial issues and to predict new phenomena
of the human motion control system. A physiologically analogous artificial
neural network
controller, a feed-forward controller, and two anatomically accurate
robotic arms (the Anthroform Arm and the testing elbow) are implemented and
applied
in this study. In order to build the physical arm systems to have mechanical
properties as close
as possible to the human arm, McKibben pneumatic artificial muscles,
force sensors, and mechanical muscle spindles are integrated in the
systems with anatomically accurate muscle attachment points.
In addition, the neural controller emulates the behavior of spinal
segmental reflex circuitry in real time, which includes motoneurons,
interneurons, and Ia and Ib afferent feedbacks. The feed-forward
controller emulates the inverse kinematics and inverse dynamics
functions of higher central nervous system to convert conceptual
movement parameters to spinal neural activation patterns.
Systematic experiments of elbow posture maintenance and voluntary fast
flexion
movement are performed and compared to physiological experimental data.
New experiments are performed in which responses to torque perturbation
are measured when selected afferent pathways are blocked. A "covariance
diagram" is introduced. And a linear model is used to help to analyze
the roles of system components and the stability. The results show: 1)
the neural controller and testing elbow system is capable of similar
performance in posture maintenance as the human elbow. 2) Muscle
co-contraction and Ia afference with gamma motoneuron excitation are
two effective ways to increase joint stiffness and damping, which in
turn reduces the mechanical sensitivity of the joint to external
perturbation and shorten the settling time of the system. 3)
Feed-forward inverse dynamics information is important to performing
voluntary fast movement. And 4) feedback delay limits the maximum loop
gain of the closed-loop controlled system to maintain stability and
still produce enough additional torque to resist external perturbations
during voluntary movement. A higher level "nearly open loop" predictive
feedback mechanism is proposed for solving this high-loop-gain
instability problem. Physiological experiments are encouraged to verify
the above phenomena in the future.
["I would like a hard copy of this report"]
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Updated: Tue Jul 15 23:54:51 2008
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