Data-Driven Retrospective Cost Adaptive Control for Flight Control Application

TL;DR

This paper introduces a data-driven adaptive control method combining retrospective cost adaptive control with recursive least squares system identification, applied to various flight control scenarios with unknown dynamics.

Contribution

It presents a novel integration of RCAC with RLS-VRF for online system identification, enhancing adaptive control for complex, uncertain flight dynamics.

Findings

Effective in handling unknown NMP zeros

Successfully applied to diverse flight control problems

Improves adaptive control performance in uncertain conditions

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.