Iterative Learning Control with Human-in-the-loop

This project will develop a new type of controller that will significantly improve the effectiveness of stroke rehabilitation technologies. The framework will be first to simultaneously assist the user while embedding a model of their own neurological motor control system within the system. This will allow it to provide effective assistance and therapy which responds to their voluntary intention and residual movement capabilities. For example, the human user may be trying to reach their arm out to pick up an object, and the control system must support their intention while maximising their contribution to the movement.
The approach will involve a substantial theoretical development of "Iterative Learning Control" (ILC) which uses the idea of learning from experience to improve tracking control, and is applied to systems that perform the same task several types. The theory will involve components of adaption, learning and robust performance design and will be tested experimentally by applying functional electrical stimulation to help users move their muscles. This component will involve a small-scale clinical trial with stroke participants facilitated by the second supervisor. This project will produce results that are ground-breaking for the development of new more effective rehabilitation and assistive technologies.