Electrode array-based electrical stimulation using ILC with restricted input subspace

Electrode arrays are gaining increasing popularity within the rehabilitation and assistive technology communities, due to their potential to deliver selective electrical stimulation to underlying muscles. This paper develops the first model-based control strategy in this area, unlocking the potential for faster, more accurate postural control. Due to time-varying nonlinear musculoskeletal dynamics, the approach fuses model identification with iterative learning control (ILC), and employs a restricted input subspace comprising only those inputs deemed critical to task completion. The subspace selection embeds past experience and/or structural knowledge, with a dimension chosen to affect a trade-off between the test time and overall accuracy. Experimental results using a 40 element surface electrode array confirm accurate tracking of three reference hand postures.