Linear decentralized learning controlThe new field of learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this task. The simplest forms of learning control are based on the same concept as integral control, but operating in the domain of the repetitions of the task. This paper studies the use of such controllers in a decentralized system, such as a robot with the controller for each link acting independently. The basic result of the paper is to show that stability of the learning controllers for all subsystems when the coupling between subsystems is turned off, assures stability of the decentralized learning in the coupled system, provided that the sample time in the digital learning controller is sufficiently short.
Document ID
19920060744
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Lee, Soo C. (NASA Langley Research Center Hampton, VA, United States)
Longman, Richard W. (Columbia University New York, United States)
Phan, Minh (Lockheed Engineering and Management Services Co., Inc. Hampton, VA, United States)