A Discrete-Time Norm-Optimal Approach to Iterative Learning Control of a Bridge Crane

摘要

In this contribution, a norm-optimal iterative learning control (NOILC) for the two main axes of a bridge crane is presented. For each axis, the NOILC operates in parallel to a linear-quadratic (LQ) state feedback of the tracking error. Regarding the tracking of repetitive trajectories, the ILC part contributes to a significant reduction of the tracking error from iteration to iteration, up to an accuracy that is determined by the quality of the measurement signals. In this paper, the ILC law is based on the minimization of a cost functional and involves both feedforward and feedback control actions. The control structure has been implemented at a bridge crane test rig with three axes, where the lateral rope deflections are determined by means of a CMOS camera. Experimental results show that a fast error convergence and a small remaining tracking error can be achieved with the proposed control structure.