Modeling Error Driven Robot Control

In this paper, we present a modeling error driven adaptive controller for control of a robot with unknown dynamics. In general, modeling error is not used since the ideal parameters are not known. However, using a feedback linearization approach we show that the modeling error can be obtained by a measured quantity representing the error dynamics under the ideal conditions, that is, the case for which the robot parameters are known a priori. We show that using this approach, the learning dynamics and plant dynamics are effectively decoupled and can then be analyzed separately. We present simulation examples of the 2-link manipulator that illustrates the algorithm. I