Tuned Modified Transpose Jacobian Control of Robotic Manipulators

The Modified Transpose Jacobian (MTJ) algorithm has been proposed, based on an approximated feedback linearization approach, in which there is no need to a priori knowledge of the plant dynamics. This paper presents a Tuned Modified Transpose Jacobian (TMTJ) control algorithm hich improves and facilitates the applicability of the MTJ algorithm, by tuning the switching gain matrix. This new control algorithm eliminates heuristic tuning for sensitivity thresholds of switching factor in the MTJ algorithm that will be useful for online control purposes. Obtained results reveal significant merits of the new proposed TMTJ controller, i.e. its performance is comparable to that of model-based algorithms and the original MTJ algorithm even in the presence of significant disturbances and noises. Moreover, extremely low computational needs, without requiring a priori knowledge of the system dynamics, makes the new proposed TMTJ algorithm a suitable candidate for position control of robotic manipulators.

Keywords:

Position control , Robotic manipulators , Tracking , Model-based algorithms , Transpose Jacobian algorithm