  Online approximation of an infinite horizon optimal path-following strategy
for a kinematic unicycle is considered. The solution to the optimal control
problem is approximated using an approximate dynamic programming technique that
uses concurrent-learning-based adaptive update laws to estimate the unknown
value function. The developed controller overcomes challenges with the
approximation of the infinite horizon value function using an auxiliary
function that describes the motion of a virtual target on the desired path. The
developed controller guarantees uniformly ultimately bounded (UUB) convergence
of the vehicle to a desired path while maintaining a desired speed profile and
UUB convergence of the approximate policy to the optimal policy. Simulation
results are included to demonstrate the controller's performance.
