Bayesian Algorithms Learn to Stabilize Unknown Continuous-Time Systems
This work addresses the challenge of ensuring stability in learning-based control for systems with uncertain dynamics, which is critical for applications like robotics and autonomous systems, though it appears incremental as it builds on existing Bayesian methods for stabilization.
The authors tackled the problem of stabilizing unknown continuous-time stochastic linear systems by proposing a Bayesian learning algorithm that effectively learns from unstable data to achieve stabilization in a finite time, with results showing effective performance after a short interaction period.
Linear dynamical systems are canonical models for learning-based control of plants with uncertain dynamics. The setting consists of a stochastic differential equation that captures the state evolution of the plant understudy, while the true dynamics matrices are unknown and need to be learned from the observed data of state trajectory. An important issue is to ensure that the system is stabilized and destabilizing control actions due to model uncertainties are precluded as soon as possible. A reliable stabilization procedure for this purpose that can effectively learn from unstable data to stabilize the system in a finite time is not currently available. In this work, we propose a novel Bayesian learning algorithm that stabilizes unknown continuous-time stochastic linear systems. The presented algorithm is flexible and exposes effective stabilization performance after a remarkably short time period of interacting with the system.