Identification of human control during walking
Jason K. Moore
Cleveland State University
February 27 2014
Exoskeletons
Don't move naturally, can't balance
Idealized Gait Feedback Control
Idealized Gait Feedback Control
Estimated
- \(\varphi\): Phase of gait cycle
- \(\mathbf{s}(\varphi)\): Joint angles and rates
- \(\mathbf{m}(\varphi)\): Joint torques
- \(w(t)\): Random belt speed
Unknown
- \(\mathbf{K}(\varphi)\): Matrix of feedback gains
- \(\mathbf{s}_0(\varphi)\): Open loop joint angles and rates
- \(\mathbf{m}_0(\varphi)\): Open loop joint torques
Block Diagram Algebra
\[
\mathbf{m} = \mathbf{m}_0(\varphi) + \mathbf{K}(\varphi) [\mathbf{s}_0(\varphi) - \mathbf{s}] \\
\]
\[
\mathbf{m} = \mathbf{m}_0(\varphi) + \mathbf{K}(\varphi) \mathbf{s}_0(\varphi) - \mathbf{K}(\varphi) \mathbf{s} \\
\]
\[
\mathbf{m} = \mathbf{m}^*(\varphi) - \mathbf{K}(\varphi) \mathbf{s}
\]
Random Belt Speed Variations
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Gains: v=1.2 m/s
Subject 1
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Subject 2
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Future
- Explore full \(\mathbf{K}\) matrix.
- Add in time delays.
- Remove clock from controller.
- Non-linear control models: e.g. neural network.
- Use indirect system identification approach with plant model.
- Try out the controller on a simulation that has open loop control.
- Try out the controller on the Indego.
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