Experimental verification of progressive learning control for high-speed direct-drive robots with structure flexibility and non-collocated sensors

Abstract

A novel approach to the learning control of non-collocated robotic systems is presented, and its feasibility and effectiveness are verified through experiments using an ultra-high speed direct-drive robot having structural flexibility. This approach, called “progressive learning,” allows the system to learn parameters recursively and progressively, starting with the ones associated with low frequencies and moving up to the ones with a full spectrum. Even though the system has non-collocated sensors and actuators and the relative order is three or higher, the learning process is guaranteed to converge by exciting the system with a particular series of reference inputs having an appropriate frequency spectrum. A controller augmentation technique to accelerate the learning process is also presented.