Reinforcement learning control for a robotic manipulator with unknown deadzone

Li, Yanan, Xiao, Shengtao and Ge, Shuzhi Sam (2015) Reinforcement learning control for a robotic manipulator with unknown deadzone. 2014 11th World Congress on Intelligent Control and Automation (WCICA), Shenyang, China, 29 June-4 July 2014. Published in: 2014 11th World Congress on Intelligent Control and Automation (WCICA). 593-598. Institute of Electrical and Electronics Engineers ISBN 9781479958252

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Abstract

In 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.

Item Type: Conference Proceedings
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Schools and Departments: School of Engineering and Informatics > Engineering and Design
Depositing User: Yanan Li
Date Deposited: 15 Dec 2017 11:13
Last Modified: 15 Dec 2017 11:13
URI: http://sro.sussex.ac.uk/id/eprint/72083

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