  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.
