Adaptive active force control of a robotic arm employing twin iterative learning algorithms / Musa Mailah and Ong Miaw Yong

The paper highlights a novel and robust method to control a robotic arm using iterative learning technique embedded in an active force control strategy. Two iterative learning algorithms are employed in the study - the first is used to tune automatically the controller gains while the second to estimate the inertia matrix of the robotic arm. These parameters are adoptively computed while the robot is executing a trajectory tracking task and subject to some form of external disturbance. No priori knowledge of both the controller gains and the estimated inertia matrix are ever assumed in the study. In this way, an adaptive and robust control scheme is derived. The effectiveness of the
method is verified and can be seen from the results of the work presented in this paper. A trajectory track control of a two-link robot arm employing the proposed scheme with a number of operating and loading conditions is investigated in the study.