Experimental evaluation of nonlinear adaptive controllers

Attractive methods for learning the dynamics and improving the control of robot manipulators during movements have been proposed for more than 10 years, but they still await applications. This article investigates practical issues for the implementation of these methods. Two nonlinear adaptive controllers, selected for their simplicity and efficiency, are tested on 2-DOF and 3-DOF manipulators. The experimental results show that the Adaptive FeedForward Controller (AFFC) is well suited for learning the parameters of the dynamic equation, even in the presence of friction and noise. The control performance along the learning trajectory and other test trajectories are also better than when measured parameters are used, However, when the task consists of driving a repeated trajectory, the adaptive lookup table MEMory is simpler to implement, It also provides a robust and stable control, and results in even better performance.