Task-Space Iterative Learning for Redundant Robotic Systems: Existence of a Task-Space Control and Convergence of Learning

Abstract

AbstractThis paper presents a feasibility study of iterative learning control for a class of redundant multi-joint robotic systems when a desired motion trajectory is specified in task-space with less dimension than that of joint space. First, it is shown that if the desired trajectory described in task-space for a time interval t ∈ [0,T] is twice continuously differentiable then a unique control signal describable in task-space exists despite of the system joint-redundancy. Second, a learning control update law is constructed through transpose of the Jacobian matrix of task-space coordinates with respect to joint coordinates by using measured data of motion trajectories in task-space. Third, the convergence of trajectory trackings through iterative learning is proved theoretically on the basis of original nonlinear robot dynamics in joint space.Keywordsiterative learningredundant robottrajectory trackingtranspose jacobian