Joint Learning-Based Stabilization of Multiple Unknown Linear Systems

Mohamad Kazem Shirani Faradonbeh, Aditya Modi

Abstract:

Learning-based control of linear systems received a lot of attentions recently. In popular settings,
the true dynamical models are unknown to the decision-maker and need to be interactively learned by
applying control inputs to the systems. Unlike the matured literature of efficient reinforcement learning
policies for adaptive control of a single system, results on joint learning of multiple systems are not
currently available. Especially, the important problem of fast and reliable joint-stabilization remains
unaddressed and so is the focus of this work. We propose a novel joint learning-based stabilization
algorithm for quickly learning stabilizing policies for all systems understudy, from the data of unstable
state trajectories. The presented procedure is shown to be notably effective such that it stabilizes the
family of dynamical systems in an extremely short time period.