Spatial Iterative Learning Control for Nonholonomic Robotics With Input Saturation

Abstract

Many industrial and field robotic applications, such as path-following for unmanned ground vehicles (UGVs), involve repetitive tasks that require high-precision tracking under physical actuator constraints. While classical iterative learning control (ILC) can achieve high accuracy when repeatedly following a given trajectory with a fixed completion time, its performance deteriorates when tracking a prescribed path whose execution time varies between trials. This article proposes a spatial iterative learning control (SILC) framework for path-following of a class of nonholonomic systems. A time projection mapping is introduced to transform trial-varying temporal dynamics into a unified spatial..