Abstract: We propose an adaptive neural control algorithm for the rigid manipulators system based on extreme learning machine (ELM). The ELM for a single-hidden layer feedforward neural network (SLFN) can analytically determine the output weights of the SLFN and randomly choose hidden nodes and its parameters, providing good generalized performance at an extremely fast learning speed. Using the Lyapunov synthesis approach, the proposed ELM controller can approximate the model uncertainy of systems by adaptively tuning the output weight to guarantees the stability of the overall closed-loop control system. The proposed adaptive neural controller is applied to control a planar manipulator with two degrees of freedom and is compared with the existing radial basis function neural control algorithms. Experiment results show that the ELM controller has good tracking performance at the same experiment conditions, which demonstrates the effectiveness of the proposed control algrorithm.