Passivity-Based Online Reinforcement Learning for Real Time Model-Free Overhead Crane System Control-Reference-Cited by-同舟云学术

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Passivity-Based Online Reinforcement Learning for Real Time Model-Free Overhead Crane System Control

Published:2022-08-15 Issue: Volume: Page:
ISSN:
Container-title:2022 34th Chinese Control and Decision Conference (CCDC)
language:
Short-container-title:

Author:

Zhang Haoran¹,Zhao Chunhui¹,Ding Jinliang²

Affiliation:

1. Zhejiang University,State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering,Hangzhou,China,310027

2. Northeastern University,State Key Laboratory of Synthetical Automation for Process Industries,Shenyang,China,110819

Funder

National Science Fund for Distinguished Young Scholars

State Key Laboratory of Industrial Control Technology

Zhejiang University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10032843/10032844/10034123.pdf?arnumber=10034123

Reference26 articles.

1. Intelligent Optimal Control With Critic Learning for a Nonlinear Overhead Crane System

2. Inverse dynamic control via “simulation of feedback control” by artificial neural networks for a crane system

3. Human-level control through deep reinforcement learning

4. Fuzzy crane control with sensorless payload deflection feedback for vibration reduction

5. A study of crane operator performance comparing PD-control and input shaping

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