Combined direct and indirect adaptive control of robot manipulators using multiple models
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 study is an extension of our previous work which utilized an indirect adaptive
control approach with multiple models for better transient performance. The proposed scheme 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.