Iterative Learning-Based Robotic Controller With Prescribed Human-Robot Interaction Force

In this article, an iterative-learning-based robotic controller is developed, which aims at providing a prescribed assistance or resistance force to the human user. In the proposed controller, the characteristic parameter of the human upper limb movement is first learned by the robot using the measurable interaction force, a recursive least square (RLS)-based estimator, and the Adam optimization method. Then, the desired trajectory of the robot can be obtained, tracking which the robot can supply the human's upper limb with a prescribed interaction force. Using this controller, the robot automatically adjusts its reference trajectory to embrace the differences between different human users with diverse degrees of upper limb movement characteristics. By designing a performance index in the form of interaction force integral, potential adverse effects caused by the time-related uncertainty during the learning process can be addressed. The experimental results demonstrate the effectiveness of the proposed method in supplying the prescribed interaction force to the human user.

Automated summary

An iterative-learning-based robotic controller is proposed in this article to provide a prescribed assistance or resistance force to human users. The controller learns the characteristic parameter of human upper limb movement using measurable interaction force, a recursive least square (RLS) estimator, and the Adam optimization method. Experimental results show the effectiveness of the proposed method in supplying the prescribed interaction force to human users.