Time delay control of nonlinear systems with neural network modeling

Mechatronics ◽  
1997 ◽  
Vol 7 (7) ◽  
pp. 613-640 ◽  
Author(s):  
Jeong-Woo Lee ◽  
Jun-Ho Oh
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Nonlinear Systems ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Time Delay Control ◽  
Delay Control

Development of the instrumental support of the domestic computing platform "Elbrus 801-PC" in the problems of neural network modeling of nonlinear dynamic systems

2021 ◽  
Author(s):  
O.V. Druzhinina ◽  
E.R. Korepanov ◽  
V.V. Belousov ◽  
O.N. Masina ◽  
A.A. Petrov
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Switching Algorithm ◽  
Computing Platform ◽  
Controlled Systems ◽  
Research Problems ◽  
Extended Analysis ◽  
Software And Hardware

The development of tools for solving research problems with the use of domestic software and hardware is an urgent direction. Such tasks include the tasks of neural network modeling of nonlinear controlled systems. The paper provides an extended analysis of the capabilities of the Elbrus architecture and the blocks of the built-in EML library for mathematical modeling of nonlinear systems. A comparative analysis of the instrumentation and efficiency of computational experiments is performed, taking into account the use of an 8-core processor and the potential capabilities of a 16-core processor. The specifics of the EML library blocks in relation to solving specific types of scientific problems is considered and the optimized software is analyzed. The design of generalized models of nonlinear systems with switching is proposed. For generalized models, a new switching algorithm has been developed that can be adapted to the Elbrus computing platform. An algorithmic tree is constructed, and algorithmic and software are developed for the study of models with switching. The results of adaptation of the modules of the software package for modeling managed systems to the elements of the platform are presented. The results of computer modeling of nonlinear systems based on the Elbrus 801-RS computing platform are systematized and generalized. The results can be used in problems of creating algorithmic and software for solving research modeling problems, in problems of synthesis and analysis of models of controlled technical systems with switching modes of operation, as well as in problems of neural network modeling and machine learning.

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 Switched time delay control based on artificial neural network for fault detection and compensation in robot manipulators

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Dihya Maincer ◽  
Moufid Mansour ◽  
Amar Hamache ◽  
Chemseddine Boudjedir ◽  
Moussaab Bounabi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Delay ◽  
Control Method ◽  
High Gain ◽  
Industrial Applications ◽  
Control Laws ◽  
Time Delay Control ◽  
Delay Control ◽  
Artificial Neural

AbstractThis work proposes a switched time delay control scheme based on neural networks for robots subjected to sensors faults. In this scheme, a multilayer perceptron (MLP) artificial neural network (ANN) is introduced to reproduce the same behavior of a robot in the case of no faults. The reproduction characteristic of the MLPs allows instant detection of any important sensor faults. In order to compensate the effects of these faults on the robot’s behavior, a time delay control (TDC) procedure is presented. The proposed controller is composed of two control laws: The first one contains a small gain applied to the faultless robot, while the second scheme uses a high gain that is applied to the robot subjected to faults. The control method applied to the system is decided based on the ANN detection results which switches from the first control law to the second one in the case where an important fault is detected. Simulations are performed on a SCARA arm manipulator to illustrate the feasibility and effectiveness of the proposed controller. The results demonstrate that the free-model aspect of the proposed controller makes it highly suitable for industrial applications.

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