Data-Driven Retrospective Cost Adaptive Control for Flight Control Application
Abstract
Unlike fixed-gain robust control, which trades off performance with modeling uncertainty, direct adaptive control uses partial modeling information for online tuning. The present paper combines retrospective cost adaptive control (RCAC), a direct adaptive control technique for sampled-data systems, with online system identification based on recursive least squares (RLS) with variable-rate forgetting (VRF). The combination of RCAC and RLS-VRF constitutes data-driven RCAC (DDRCAC), where the online system identification is used to construct the target model, which defines the retrospective performance variable. This paper investigates the ability of RLS-VRF to provide the modeling information needed for the target model, especially nonminimum-phase (NMP) zeros. DDRCAC is applied to single-input, single-output (SISO) and multiple-input, multiple-output (MIMO) numerical examples with unknown NMP zeros, as well as several flight control problems, namely, unknown transition from minimum-phase to NMP lateral dynamics, flexible modes, flutter, and nonlinear planar missile dynamics.
Cite
@article{arxiv.2102.07191,
title = {Data-Driven Retrospective Cost Adaptive Control for Flight Control Application},
author = {Syed Aseem Ul Islam and Tam W. Nguyen and Ilya V. Kolmanovsky and Dennis S. Bernstein},
journal= {arXiv preprint arXiv:2102.07191},
year = {2021}
}
Comments
60 pages, 28 figures, accepted by AIAA Journal of Guidance, Control, and Dynamics