scholarly journals Feedback linearization based predictor for time delay control of a high-DOF robot manipulator

Automatica ◽  
2019 ◽  
Vol 108 ◽  
pp. 108485 ◽  
Author(s):  
Mostafa Bagheri ◽  
Peiman Naseradinmousavi ◽  
Miroslav Krstić
Keyword(s):  
Time Delay ◽  
Feedback Linearization ◽  
Robot Manipulator ◽  
Time Delay Control ◽  
Delay Control

scholarly journals Switched time delay control based on neural network for fault detection and compensation in robot

2021 ◽  
Vol 10 (2) ◽  
pp. 91
Author(s):  
Maincer Dihya ◽  
Mansour Moufid ◽  
Boudjedir Chemseddine ◽  
Bounabi Moussaab
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Fault Detection ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Neural Network Learning ◽  
Mlp Neural Network ◽  
Time Delay Control ◽  
The Neural Network ◽  
Delay Control

Fault detection in robotic manipulators is necessary for their monitoring and represents an effective support to use them as independent systems. This present study investigates an enhanced method for representation of the faultless system behavior in a robot manipulator based on a multi-layer perceptron (MLP) neural network learning model which produces the same behavior as the real dynamic manipulator. The study was based on generation of residue by contrasting the actual output of the manipulator with those of the neural network; Then, a time delay control (TDC) is applied to compensate the fault, in which a typical sliding mode command is used to delete the time delay estimate produced by the belated signal in order to obtain strong performances. The results of the simulations performed on a model of the SCARA arm manipulator, showed a good trajectory tracking and fast convergence speed in the presence of faults on the sensors. In addition, the command is completely model independent, for both TDC and MLP neural network, which represents a major advantage of the proposed command.

scholarly journals Time-delay control for stabilization of the Shapovalov mid-size firm model

2020 ◽  
Vol 53 (2) ◽  
pp. 16971-16976
Author(s):  
T.A. Alexeeva ◽  
W.A. Barnett ◽  
N.V. Kuznetsov ◽  
T.N. Mokaev
Keyword(s):  
Time Delay ◽  
Time Delay Control ◽  
Delay Control ◽  
Size Firm ◽  
Firm Model

scholarly journals Robust-adaptive dynamic programming-based time-delay control of autonomous ships under stochastic disturbances using an actor-critic learning algorithm

2021 ◽  
Author(s):  
Hossein Nejatbakhsh Esfahani ◽  
Rafal Szlapczynski
Keyword(s):  
Dynamic Programming ◽  
Time Delay ◽  
Control Algorithm ◽  
Approximate Dynamic Programming ◽  
Robust Adaptive Control ◽  
Stochastic Disturbances ◽  
Time Delay Control ◽  
Model Free ◽  
Delay Control ◽  
Robust Adaptive

AbstractThis paper proposes a hybrid robust-adaptive learning-based control scheme based on Approximate Dynamic Programming (ADP) for the tracking control of autonomous ship maneuvering. We adopt a Time-Delay Control (TDC) approach, which is known as a simple, practical, model free and roughly robust strategy, combined with an Actor-Critic Approximate Dynamic Programming (ACADP) algorithm as an adaptive part in the proposed hybrid control algorithm. Based on this integration, Actor-Critic Time-Delay Control (AC-TDC) is proposed. It offers a high-performance robust-adaptive control approach for path following of autonomous ships under deterministic and stochastic disturbances induced by the winds, waves, and ocean currents. Computer simulations have been conducted under two different conditions in terms of the deterministic and stochastic disturbances and all simulation results indicate an acceptable performance in tracking of paths for the proposed control algorithm in comparison with the conventional TDC approach.

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