Data-Guided Regulator for Adaptive Nonlinear Control
- URL: http://arxiv.org/abs/2311.12230v1
- Date: Mon, 20 Nov 2023 23:02:39 GMT
- Title: Data-Guided Regulator for Adaptive Nonlinear Control
- Authors: Niyousha Rahimi and Mehran Mesbahi
- Abstract summary: This paper addresses the problem of a data-driven feedback controller for complex nonlinear systems.
The goal is to achieve finite-time regulation of system states through direct policy updates.
- Score: 0.27195102129094995
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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