Reinforcement learning-based fixed-time trajectory tracking control for uncertain robotic manipulators with input saturation

A fixed-time trajectory tracking control method for uncertain robotic manipulators with input saturation based on reinforcement learning (RL) is studied. The designed reinforcement learning control algorithm is implemented by radial basis function (RBF) neural network, in which the actor neural netw...

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Bibliographic Details
Main Authors: Shengjie Cao, Liang Sun, Jingjing Jiang, Zongyu Zuo
Format: Default Article
Published: 2021
Subjects:
uncertain robotic manipulators
trajectory tracking control
reinforcement learning
fixed-time control
input saturation
non-singular terminal sliding mode control
Online Access:https://hdl.handle.net/2134/16691437.v1
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https://hdl.handle.net/2134/16691437.v1

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