Extreme learning machine based robotic arm modeling

Robotic arms are very powerful machines that can be used in many various applications in industry. So that, a suitable dynamic model is derived to verify that performs the tasks. But, dynamic equation is an important issue due to its complexity. Thus, an alternative model can be derived for the robotic arms. This paper is proposed Extreme Learning Machine (ELM) model for the angular acceleration of a robotic arm. The performance of the ELM model is performed by using Pumadyn datasets. At the same time, the validation of the proposed model is compared with Artificial Neural Network (ANN). Experimental results show that the proposed model is suitable and it provides low computation complexity.