Adaptive Control of 3-DOF Delta Parallel Robot

Abstract

In this paper an adaptive neural network controller is used to solve the problem of tracking trajectories of a delta parallel robot (DPR) with three degrees of freedom. This controller used an adaptive artificial B-Spline neural network (BSNN) for online training. The BSNN improves the performance of DPR on a closed loop and update the parameters of control scheme online. This algorithm sets the control signal without using a detailed mathematical model nor exact values of the parameters of the DPR. The proposed adaptive controller was compared with a traditional control based a PD +G contoller. Analytical and numerical results prove the robust and efficient performance of the adaptive neural network controller.