Adapting robust controllers to a changing human compliance

When robot systems enter into a mechanical feedback relationship with humans, the closed-loop system must remain stable despite any unmodeled high-frequency dynamics in order to be deemed safe, and this imposes a bandwidth limit on performance. However, humans are difficult to model accurately for feedback purposes. Their behavior is never perfectly known nor is it consistent. My team’s work at The University of Texas at Austin has shown that if a robot can infer the changing human compliance, it can update its control strategy to significantly improve strength amplification performance [HuangCappelThomasHeSentis2020ACC]. However, there is a fundamental gap in knowledge on how to reconcile the desire for formal stability guarantees with the adaptive control theory that allows feedback controllers to make these changes. I hypothesize that these goals can be simultaneously achieved by the online synthesis of both a bounded-uncertainty model to explain past measurements and a controller that robustly stabilizes this model. My preliminary work towards this topic \cite{ThomasSentis2019TAC} has demonstrated for the first time that direct synthesis of a bounded-uncertainty model is possible and that it is even a convex optimization problem, a problem class that can be solved reliably by numerical algorithms. Tools exist to design adaptive controllers that are robust in the sense of modeled disturbances of known relative order but not in the sense of guaranteed stability despite unmodeled dynamics. The overall goals of this research thrust are to (1) extend the framework for robust model identification into a complete theoretical framework for online synthesis of uncertain models and robust controllers, (2) establish the feasibility of adapting exoskeleton controllers to identified uncertain human models in a 2-DOF strength amplifying co-bot testbed, and (3) determine the extent to which the framework can scale to larger systems given realistic limits on computational power. This research is aligned with the mission of the NSF CMMI program in Dynamics, Control and Systems Diagnostics.