Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation
This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN) and disturbance observer. First, the RBFNN is used to approximate the system uncertainty, and the unknown approximation...
Main Authors: | Rong Mei, ChengJiang Yu |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi-Wiley
2017-01-01
|
Series: | Complexity |
Online Access: | http://dx.doi.org/10.1155/2017/7413642 |
Similar Items
-
Adaptive T-S Fuzzy Output Feedback Tracking Control of Uncertain Constrained Robot Manipulators
by: RUI-YU CHEN, et al.
Published: (2010) -
ADAPTIVE FUZZY OUTPUT-FEEDBACK SLIDING MODE CONTROL FOR SWITCHED UNCERTAIN NONLINEAR SYSTEMS WITH INPUT SATURATION
by: Wei-Tse Kao, et al.
Published: (2018) -
Neural network adaptive command filtered control of robotic manipulators with input saturation
by: Lin Wang, et al.
Published: (2019-12-01) -
Output Feedback Control via Linear Extended State Observer for an Uncertain Manipulator with Output Constraints and Input Dead-Zone
by: Duc Thien Tran, et al.
Published: (2020-08-01) -
Output-feedback proportional-integral-derivative-type control with multiple saturating structure for the global stabilization of robot manipulators with bounded inputs
by: Arturo Zavala-Rio, et al.
Published: (2016-09-01)