Combined direct and indirect adaptive control of robot manipulators using multiple models

In this paper a novel methodology is proposed for the adaptive control of rigid robotic manipulators. The proposed method utilizes multiple adaptive models for the identification and control of the manipulator. The present work is an extension of our previous work which utilized an indirect adaptive control approach with multiple models. The proposed scheme in this paper uses a composite approach where both prediction and tracking errors are used in a combined direct and indirect adaptive control framework. Simulation results are given to demonstrate the efficient use of the methodology.