Abstract:
Most of the studies on rehabilitation robots consider
the human arm inertia and the gravity torque as system
disturbances. Individual anthropometry varies from patient to
patient, and therefore human limbs are not modelled. Some
studies used the Disturbance Observer (DOB) as a method of
disturbance rejection. However, if the inertia and gravity torque
parameters of the human arm could be estimated, they could
be effectively used in the controller loop to achieve precise
motion control. This paper proposes a novel Reaction Torque
Observer (RTOB) based estimation technique which updates
parameters using learning and recursive algorithms in real-time.
The proposed method is applicable to many robot systems where
the load inertia or the load is not known. A simulation was
carried out with realistic parameters to compare the performance
of two competing methods proposed namely, Adaptive Linear
Neuron (ADALINE) and Recursive Least Squares (RLS). Results
show that the RLS method outperforms the ADALINE method
based on the performance criteria of accuracy, precision and
convergence speed for estimating the inertia.
Citation:
P. A. Diluka Harischandra, A. M. Harsha and S. Abeykoon, "Simulation of Online Human Arm Inertia Estimation for Robot-aided Rehabilitation," 2018 Moratuwa Engineering Research Conference (MERCon), 2018, pp. 31-36, doi: 10.1109/MERCon.2018.8421998.