  Convergence of controller parameters in standard model reference adaptive
control (MRAC) requires the system states to be persistently exciting (PE), a
restrictive condition to be verified online. A recent data-driven approach,
concurrent learning, uses information-rich past data concurrently with the
standard parameter update laws to guarantee parameter convergence without the
need of the PE condition. This method guarantees exponential convergence of
both the tracking and the controller parameter estimation errors to zero,
whereas, the classical MRAC merely ensures asymptotic convergence of tracking
error to zero. However, the method requires knowledge of the state derivative,
at least at the time instances when the state values are stored in memory. The
method further assumes knowledge of the control allocation matrix. This paper
addresses these limitations by using a memory-based finite-time system
identifier in conjunction with a data-driven approach, leading to convergence
of both the tracking and the controller parameter estimation errors without the
PE condition and knowledge of the system matrices and the state derivative. A
Lyapunov based stability proof is included to justify the validity of the
proposed data-driven approach. Simulation results demonstrate the efficacy of
the suggested method.
