Data-Guided Regulator for Adaptive Nonlinear Control
Niyousha Rahimi Mehran Mesbahi
This paper addresses the problem of designing a data-driven feedback
controller for complex nonlinear dynamical systems in the presence of
time-varying disturbances with unknown dynamics. Such disturbances are modeled
as the "unknown" part of the system dynamics. The goal is to achieve
finite-time regulation of system states through direct policy updates while
also generating informative data that can subsequently be used for data-driven
stabilization or system identification. First, we expand upon the notion of
"regularizability" and characterize this system characteristic for a linear
time-varying representation of the nonlinear system with locally-bounded
higher-order terms. "Rapid-regularizability" then gauges the extent by which a
system can be regulated in finite time, in contrast to its asymptotic behavior.
We then propose the Data-Guided Regulation for Adaptive Nonlinear Control (
DG-RAN) algorithm, an online iterative synthesis procedure that utilizes
discrete time-series data from a single trajectory for regulating system states
and identifying disturbance dynamics. The effectiveness of our approach is
demonstrated on a 6-DOF power descent guidance problem in the presence of
adverse environmental disturbances.