THESIS
2022
1 online resource (xix, 130 pages) : illustrations (some color)
Abstract
Recently, lower-limb exoskeletons have demonstrated the ability to enhance human
mobility and walking efficiency for both healthy subjects and patients. However, this
technology is confined to the laboratory and its performance is unsatisfactory in a community
environment due to: 1) as important components of the exoskeletons, the inertial
measurement units (IMUs) are vulnerable to external acceleration and magnetic disturbance.
2) the control performance is significantly affected by external disturbance. 3) the
user’s preference or the individualized walking coordination strategy is rarely considered
in the assistance map construction.
This thesis aims to cope with the aforementioned issues. Specifically, the first two
issues can be regarded as performance degeneration of the state est...[
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Recently, lower-limb exoskeletons have demonstrated the ability to enhance human
mobility and walking efficiency for both healthy subjects and patients. However, this
technology is confined to the laboratory and its performance is unsatisfactory in a community
environment due to: 1) as important components of the exoskeletons, the inertial
measurement units (IMUs) are vulnerable to external acceleration and magnetic disturbance.
2) the control performance is significantly affected by external disturbance. 3) the
user’s preference or the individualized walking coordination strategy is rarely considered
in the assistance map construction.
This thesis aims to cope with the aforementioned issues. Specifically, the first two
issues can be regarded as performance degeneration of the state estimation with some
channels containing non-Gaussian noises. In the conventional estimation framework, the
mean squared error (MSE) has been widely used as a cost function due to its features
of smoothness, convexity, and mathematical tractability. However, the optimality of the
MSE is rooted in the Gaussian assumption, and its performance may be unsatisfactory
with heavy-tailed noises. Actually, the correntropy is a better metric for non-Gaussian
noises since it captures high-order error statistics. However, it is defined for random
variables and is incapable of systems with partial non-Gaussian noises. In this thesis, we
introduce a multi-kernel correntropy (MKC) which extends the definition of correntropy
from random variables to random vectors. Some important properties of the MKC are
given, and a multi-kernel correntropy Kalman filter (MKMCKF) is derived based on the
MKC. The proposed estimator is robust against non-Gaussian noises and maintains good performance under Gaussian noises. Simulations verify the effectiveness of the proposed
method.
We further apply the MKMCKF to orientation estimation of IMUs and the disturbance
estimation of an exoskeleton. Specifically, for six-axis IMUs, we derive a compact
multi-kernel maximum correntropy Kalman filter (CMKMCKF) which has a minimal
parameter number and has less computation penalty. For nine-axis IMUs, we tune the
kernel bandwidths of the MKMCKF using Bayesian optimization. The proposed algorithms
are compared with the benchmark methods. Simulations and experiments verify
the effectiveness of the proposed algorithms, especially with external acceleration and
magnetic disturbance. The MKC is further extended to a generalized multi-kernel correntropy
(GMKC) under generalized Gaussian kernels. Comprehensive properties of the
GMKC are given and the corresponding generalized multi-kernel correntropy loss (GMKCL)
is introduced, which is proven to be more versatile than the traditional least mean p
power (LMP) criterion. We reveal that the GMKCL is associated with a certain class
of heavy-tailed distributions and is an optimal cost function based on the maximum a
posteriori probability under some assumptions. Then, a new filter named the generalized
multi-kernel maximum correntropy Kalman filter (GMKMCKF) is derived under
the GMKCL, and it is utilized as a disturbance observer for a target tracking task using
exoskeletons. Simulations show that the proposed disturbance observer outperforms the
existing approaches.
To involve the user’s preference in the control of the exoskeleton, a robust adaptive oscillator
(RAO) is designed to estimate the gait phase and extract gait features. Meanwhile,
the participant’s preferred assistance parameters and gait features are collected and stored.
Then, the Gaussian process regression (GPR) is employed to construct the individualized
assistance map based on the historical data. The effectiveness of the proposed method is
validated by a hip exoskeleton at a speed of 5 km/h with 7 participants. Three muscles
which include rectus femoris, tibialis anterior, and medial gastrocnemius are investigated
in three conditions: user-preferred assistance (ASS), zero torque (ZT), and normal walking
(NW). Results show that all muscles achieve an activity reduction in the ASS mode
compared with the ZT or NW. Meanwhile, there is a statistically significant difference in
medial gastrocnemius in the ASS mode with respect to the ZT and NW (−15.63±6.51%
and −8.73±6.40%, respectively).
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