In order to implement model-based recognition of
human motion intention, dynamics modeling and identification of
a lower limb rehabilitation robot named iLeg is investigated. Due
to the relatively strong motion constraints, the traditional identification methods become insufficient for iLeg in three aspects:
1) the coupling factors among joints have not been considered
in the traditional joint friction models, which makes the structural error and the torque estimation errors relatively large;
2) because of the small and complicated feasible region caused
by the motion constraints, the traditional initialization strategy,
for searching the valid initial solutions of the optimization problem for the exciting trajectories, becomes very inefficient; and
3) the condition number of the observation matrix, calculated
from the preliminary dynamic model and the associated optimized exciting trajectory, is too large for the identification, and,
however, further reduction of the condition number has not been
considered in the literature. Therefore, corresponding contributions are presented to overcome the limitation. First, the coupling
factors among joints are considered in the joint friction model by
using the Palmgren empirical formulation and a polynomial fitting method. Then, an indirectly generating strategy is designed,
by which the valid initial solutions of the optimization problem can be found with good efficiency. Moreover, a recursive
optimization method based on the optimization of the dynamic
model and the exciting trajectories, is proposed to further reduce
the condition number. Finally, the performance of the proposed
methods is demonstrated by several experiments.