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 iterative learning control (ILC) update structures. Optimal models of movement are introduced using a cost function that is motivated by learned human motion results. Three general ILC update structures are derived that each generate the required limiting solution using different forms of experimental data. It is shown how the parameters in each that govern convergence permit 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.