Data-Driven Retrospective Cost Adaptive Control for Flight Control Applications
- 1 October 2021
- journal article
- research article
- Published by American Institute of Aeronautics and Astronautics (AIAA) in Journal of Guidance, Control, and Dynamics
- Vol. 44 (10), 1732-1758
- https://doi.org/10.2514/1.g005778
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 non-minimum-phase (NMP) zeros. DDRCAC is applied to single-input, single-output and multiple-input, multiple-output 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.Keywords
Funding Information
- Office of Naval Research (BRC grant N00014-18-1-2211)
- Air Force Office of Scientific Research (DDDAS grant FA9550-18-1-0171, FA9550-20-1-0028)
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