Parameterised function ILC with application to stroke rehabilitation

Functional electrical stimulation (FES) is a popular assistive technology that uses electrical impulses to artificially stimulate muscles to help paralysed or impaired subjects regain their lost movement after stroke. A large number of FES elements can be combined to form FES arrays which are capable of activating the multiple muscles needed to perform functional arm movements. However, the control of FES arrays is challenging since high precision is required but there is little time available in a clinical or home setting to identify a model. To date, by far the highest accuracy has been achieved using iterative learning control (ILC), a technique that mirrors the repeated nature of rehabilitation task practice. In particular, high accuracy has been achieved using a well-known ILC law for a general class of nonlinear systems which computes the updated control input using a linearised plant model. Since a global system model is unavailable, this is identified on every ILC trial by running an identification test. This adds many time-consuming identification tests, making it infeasible for clinical deployment. To solve this problem, an approach is developed that can deliver high accuracy with minimal identification overhead. It introduces a parameterised plant model that is updated in parallel with the ILC using all available data, and then applied to replace identification tests. Rigorous conditions are derived to ensure convergence is preserved while minimising identification time. Numerical results show that four references can be tracked using only 10.8% of the experimental tests required by standard ILC algorithms. The approach is then applied experimentally to six unimpaired subjects using a realistic rehabilitation scenario. In particular, a novel stereo camera system is used to measure hand joint angles in a manner that can transfer to home use. Results show mean joint angle tracking accuracy within 5 degrees, while requiring only between 25% and 64.9% of the experimental tests of standard ILC.