Abstract:

This paper addresses the recursive joint inference (state estimation) and learning (system identification) problem for nonlinear systems admitting a state-space formulation. We model the system as a Gaussian-process state-space model (GP-SSM) and leverage a recently developed reduced-rank formulation of GP-SSMs to enable efficient, online learning. The unknown dynamical system is expressed as a basis-function expansion, where a connection to the GP makes it possible to systematically assign priors to the basis-function weights. The approach is formulated within the sequential Monte-Carlo framework, wherein each particle retains its own weights of the basis functions, which are updated recursively as measurements arrive. We report competitive results when compared to a state-of-the art offline Bayesian learning method. We also apply the method to a case study concerning tire-friction estimation. The results indicate that our method can accurately learn the tire friction using automotive-grade sensors in an online setting, and quickly detect sudden changes of the road surface.