Iterative Learning Control in Task-space for Robots with Redundant Joints

Abstract

This paper proposes an iterative learning control (ILC) method for robots with redundant joints to acquire the desired control input signals that produce an endpoint trajectory specified in task space. The learning update law of control input signals is constructed only in task space by modifying the previous control input through adding linearly endpoint position and velocity trajectory errors. Although the dimension of the task space is strictly less than the DOF (Degree-of-freedom) of robots, the proposed method need neither consider any inverse kinematics problem nor introduce any cost function to be optimized and determine the inverse kinematics uniquely. Convergence of trajectory trackings to the specified one is shown by numerical simulations in both cases (1) free-endpoint motion and (2) constraint-endpoint motion with specified contact force. A theoretical proof of convergences is also given on the basis of a dynamics linearized around a joint trajectory when the endpoint tracks a desired trajectory.