On Experimentally Validated Iterative Learning Control in Human Motor Systems

A framework is developed to construct computational models of the human motor system (HMS) using various iterative learning control (ILC) update structures. Optimal models of movement are introduced using a general cost function (involving both tracking objective and an additional constraint term), and its parameters are fitted to observed limiting solutions corresponding to learned human motion obtained from experiments. Three general ILC update structures are considered which each generate the required limiting solution using different forms of experimental data. It is shown how the parameters in each which govern convergence may also be fitted to experimental learning data, with the different ILC structures permitting varying degrees of freedom in capturing the observed learning transients. Experimental results in which a participant uses a planar robot to perform reaching tasks confirm the ability of the proposed ILC structures to accurately model the learning ability of the human motor system