An adaptive neural control algorithm is proposed for completely unknown robot with only output measurement
using RBF networks and high-gain observer. The designed adaptive neural controller not only guarantees uniformly
ultimately bounded of all signals in the closed-loop system, but also achieves the deterministic learning of the unknown
closed-loop system dynamics along periodic tracking orbit. The learned knowledge can be used to improve control
performance, and can also be recalled and reused in the same or similar control task to save time and energy. Simulation
results show the effectiveness of the proposed approach.