  Real-world control applications in complex and uncertain environments require
adaptability to handle model uncertainties and robustness against disturbances.
This paper presents an online, output-feedback, critic-only, model-based
reinforcement learning architecture that simultaneously learns and implements
an optimal controller while maintaining stability during the learning phase.
Using multiplier matrices, a convenient way to search for observer gains is
designed along with a controller that learns from simulated experience to
ensure stability and convergence of trajectories of the closed-loop system to a
neighborhood of the origin. Local uniform ultimate boundedness of the
trajectories is established using a Lyapunov-based analysis and demonstrated
through simulation results, under mild excitation conditions.
