  This paper provides an approximate online adaptive solution to the
infinite-horizon optimal tracking problem for control-affine continuous-time
nonlinear systems with unknown drift dynamics. Model-based reinforcement
learning is used to relax the persistence of excitation condition. Model-based
reinforcement learning is implemented using a concurrent learning-based system
identifier to simulate experience by evaluating the Bellman error over
unexplored areas of the state space. Tracking of the desired trajectory and
convergence of the developed policy to a neighborhood of the optimal policy are
established via Lyapunov-based stability analysis. Simulation results
demonstrate the effectiveness of the developed technique.
