Non-repetitive trajectory tracking for joint position constrained robot manipulators using iterative learning control

In this paper, a novel iterative learning control (ILC) algorithm is presented for a class of joint position constrained robot manipulator systems. Unlike the traditional ILC probelms, where the reference trajectory is iteration invariant, the reference trajectory in this work can be non-repetitive over the iteration domain. A tan-type time-varying Barrier Lyapunov Function (BLF) is proposed to deal with the constraint requirements which can be both time and iteration varying. We show that under the proposed ILC scheme, uniform convergence of the full state tracking error beyond a small time interval in each iteration can be guaranteed over the iteration domain, while the constraints on the joint position vector will not be violated during operation. An illustrative example is presented in the end to demonstrate the effectiveness of the proposed control scheme.