Time Delay Compensation of a Robotic Arm based on Multiple Sensors for Indirect Teaching

Autor:	Jinuk Bang, Jang-Myung Lee, Dong-Eon Kim, Xiaolu Zhang
Rok vydání:	2021
Předmět:	Loop (topology) Artificial neural network Control theory Computer science Mechanical Engineering Control (management) PID controller Electrical and Electronic Engineering Interference (wave propagation) Robotic arm Industrial and Manufacturing Engineering Compensation (engineering) Smith predictor
Zdroj:	International Journal of Precision Engineering and Manufacturing. 22:1841-1851
ISSN:	2005-4602 2234-7593
DOI:	10.1007/s12541-021-00542-w
Popis:	In this paper, a remote-control system for a six-degree-of-freedom robotic arm that uses an indirect teaching method is proposed. In the indirect teaching method, an essential time delay occurs, which degrades the system performance. To overcome this time delay, which can be modeled using a Smith predictor, a model neural network (MNN) has been adopted. The Smith predictor is a model-based algorithm that is uncertain and prone to interference. In this study, the MNN has been utilized in an effective manner to model the system to support the Smith predictor algorithm. Using this time delay compensation, the outer loop proportional, integral, and derivative (PID) control gains are adjusted in an optimal manner through a PID neural network (PIDNN) to ensure that the robotic arm follows human commands precisely. By using the PIDNN proposed in this paper, the time required for indirect teaching application of the robot arm can be reduced.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::470417dd9327ebbcc9acb00e7a59bc45 https://doi.org/10.1007/s12541-021-00542-w Zobrazit plný text záznamu Full text from SpringerLink