Intelligent adaptive active force control of a robotic arm with embedded iterative learning algorithms

Abstract

The robust and accurate control of a robotic arm or manipulator are of prime importance especially if the system is subjected to varying forms of loading and operating conditions. The paper highlights a novel and robust method to control a robotic arm using an iterative learning technique embedded in an active force control strategy. Two main iterative learning algorithms are utilized in the study – the first is used to automatically tune the controller gains while the second to estimate the inertia matrix of the manipulator. These parameters are adaptively computed on-line while the robot is executing a trajectory tracking task and subject to some forms of external disturbances. 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.