Experiments using approximate optimal path following with concurrent learning

Online approximation of an infinite horizon optimal path following strategy for a unicycle-type mobile robot is considered. An approximate optimal guidance law is obtained by using an adaptive dynamic programming technique that uses concurrent-learning-based adaptive update laws to estimate the unknown optimal policy. The developed guidance law overcomes challenges with the approximation of the infinite horizon value function by using an auxiliary function that describes the motion of a virtual target on the desired path. The developed controller guarantees uniformly ultimately bounded convergence of the approximate policy to the optimal policy and the vehicle state to the desired path while maintaining a desired speed profile without requiring persistence of excitation. Simulation and experimental results are included to demonstrate the controller´s performance.