Iterative Learning Control for Robotic Path Following With Trial-Varying Motion Profiles

Iterative learning control (ILC) aims to maximize the performance of systems performing repeated tracking tasks. However, in most existing applications, motion profile is inherently specified a priori , which has restricted both its application range and scope improvement. For example, typical path-following task robotics only requires a spatial path rather than temporal trajectory profile, for ILC designs are unsuitable. To handle this requirement, article extends description by relaxing postulate enable trial-varying formulate an problem with system constraints. Under extended setup, algorithm proposed efficient implementation robust convergence analysis, updates input signal at end each trial reduce error. This implemented experimentally on gantry robot test platform verify performance, practical feasibility, reliability. Comparisons other methods also made clarify advantages, such as error reduction, effort constraint handling.