1387_Paper.pdf (202.49 kB)
Reinforcement learning control for a robotic manipulator with unknown deadzone
conference contribution
posted on 2023-06-09, 09:24 authored by Yanan LiYanan Li, Shengtao Xiao, Shuzhi Sam GeIn this paper, an actor critic neural network control is developed for a robotic manipulator. Both system uncertainties and unknown deadzone are considered in the tracking control design. Stability of the closed-loop system is analyzed via the Lyapunov’s direct method. The critic neural network is used to estimate the cost-to-go and the actor neural network is used to make the cost-to-go converge. Simulation studies are conducted to examine the effectiveness of the proposed actor critic neural network control.
History
Publication status
- Published
File Version
- Accepted version
Journal
2014 11th World Congress on Intelligent Control and Automation (WCICA)Publisher
Institute of Electrical and Electronics EngineersExternal DOI
Page range
593-598Event name
2014 11th World Congress on Intelligent Control and Automation (WCICA)Event location
Shenyang, ChinaEvent type
conferenceEvent date
29 June-4 July 2014Book title
Proceeding of the 11th World Congress on Intelligent Control and AutomationISBN
9781479958252Department affiliated with
- Engineering and Design Publications
Notes
(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2017-12-15First Open Access (FOA) Date
2017-12-15First Compliant Deposit (FCD) Date
2017-12-14Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC