  Model-reference adaptive systems refer to a consortium of techniques that
guide plants to track desired reference trajectories. Approaches based on
theories like Lyapunov, sliding surfaces, and backstepping are typically
employed to advise adaptive control strategies. The resulting solutions are
often challenged by the complexity of the reference model and those of the
derived control strategies. Additionally, the explicit dependence of the
control strategies on the process dynamics and reference dynamical models may
contribute in degrading their efficiency in the face of uncertain or unknown
dynamics. A model-reference adaptive solution is developed here for autonomous
systems where it solves the Hamilton-Jacobi-Bellman equation of an error-based
structure. The proposed approach describes the process with an integral
temporal difference equation and solves it using an integral reinforcement
learning mechanism. This is done in real-time without knowing or employing the
dynamics of either the process or reference model in the control strategies. A
class of aircraft is adopted to validate the proposed technique.
