Data-Guided Regulator for Adaptive Nonlinear Control
This addresses the challenge of adaptive control for nonlinear systems under disturbances, which is incremental by building on regularizability concepts.
The paper tackles the problem of designing a data-driven feedback controller for complex nonlinear dynamical systems with time-varying disturbances of unknown dynamics, achieving finite-time regulation of system states and generating informative data for stabilization or identification, as demonstrated on a 6-DOF power descent guidance problem.
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.