Adaptive control of robot manipulators with controller/update law modularity

Abstract

In this paper, we present an adaptive link position tracking controller for robot manipulators which achieves controller/update law modularity. Specifically, the proposed torque input control law is designed to (i) achieve input-to-state stability with respect to the parameter estimation error, and (ii) guarantee asymptotic link position tracking and boundedness of all closed-loop signals for any adaptive update law that satisfies some generic properties. Simulation and experimental results are included to illustrate the advantages of the proposed adaptive control law. Specifically, the proposed controller with a least-squares estimator is compared to a well-known, gradient update-based, adaptive controller. The results indicate an improvement in the tracking performance with the proposed controller.