IDENTIFICATION AND ADAPTIVE-CONTROL OF ROBOTS

The paper deals with the model identification of industrial robots as the basis for control algorithm design. The first part discusses the classical decentralized joint control using internal current and velocity loops and external position control, where the plant to be identified is the black-box containing the internal loops and an integrator. The paper presents the identification of a SCARA robot using LS, GLS and IV methods and real closed loop data under normal working conditions. The second part discusses the convergence test of selftuning adaptive control for SCARA and PUMA robots using the advanced robot control algorithm of Slotine and Li to find the unknown parameters of the robot dynamic model. The convergence has been tested by using a transputer based simulation system and the symbolic form of the robot dynamic model.