RBFNN CONTROL OF A TWO-LINK FLEXIBLE MANIPULATOR INCORPORATING PAYLOAD

Abstract

This paper presents the dynamic modeling and control of a two-link flexible robot manipulator. A dynamic model of the system is developed using a combined Euler-Lagrange and assumed mode methods. The most problems of a two-link flexible manipulator are vibration of flexible link and unpredictable payload additional. Previous researchers have discussed control methods for this system; few presented the effect of a payload profile. The simulation in this research is performed to assess the dynamic model and system responses at the hub and end-point of both links are presented and analyzed in time domains and show the advantages using radial basis function neural network (RBFNN) controller for solving flexible link vibration, achieve high-precision position tracking, and payload effect robustness. The results achieved by the proposed controller are compared with conventional PID to substantiate and verify the advantages the proposed scheme and its promising potential in control of a two-link flexible manipulator. It is shown that smaller overshoot, quickly steady state response and tracking performance of the proposed controller is good profile and better than PID controllers.