Tuning PID Controller by Neural Network for Robot Manipulator Trajectory Tracking

Ziegler and Nichols proposed the well-known Ziegler-Nichols method to tune the coefficients of PID controller. This tuning method is simple and gives fixed values for the coefficients which make PID controller have weak adaptabilities for the model parameters variation and changing in operating con...

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Autor principal:	Saad Zaghlul Saeed Al-Khayyt
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2017
Materias:	PID controller Neural Network Self tuning controller Robot manipulator Trajectory tracking Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/75fa0ccdd2124970914ea9f74862871c
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spelling	oai:doaj.org-article:75fa0ccdd2124970914ea9f74862871c2021-12-02T07:16:39ZTuning PID Controller by Neural Network for Robot Manipulator Trajectory Tracking1818-11712312-0789https://doaj.org/article/75fa0ccdd2124970914ea9f74862871c2017-12-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/155https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 Ziegler and Nichols proposed the well-known Ziegler-Nichols method to tune the coefficients of PID controller. This tuning method is simple and gives fixed values for the coefficients which make PID controller have weak adaptabilities for the model parameters variation and changing in operating conditions. In order to achieve adaptive controller, the Neural Network (NN) self-tuning PID control is proposed in this paper which combines conventional PID controller and Neural Network learning capabilities. The proportional, integral and derivative (KP, KI, KD) gains are self tuned on-line by the NN output which is obtained due to the error value on the desired output of the system under control. The conventional PID controller in the robot manipulator is replaced by NN self tuning PID controller so as to achieve trajectory tracking with minimum steady-state error and improving the dynamic behavior (overshoot). The simulation results showed that the proposed controller has strong self-adaptability over the conventional PID controller. Saad Zaghlul Saeed Al-KhayytAl-Khwarizmi College of Engineering – University of BaghdadarticlePID controllerNeural NetworkSelf tuning controllerRobot manipulatorTrajectory trackingChemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 9, Iss 1 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	PID controller Neural Network Self tuning controller Robot manipulator Trajectory tracking Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	PID controller Neural Network Self tuning controller Robot manipulator Trajectory tracking Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Saad Zaghlul Saeed Al-Khayyt Tuning PID Controller by Neural Network for Robot Manipulator Trajectory Tracking
description	Ziegler and Nichols proposed the well-known Ziegler-Nichols method to tune the coefficients of PID controller. This tuning method is simple and gives fixed values for the coefficients which make PID controller have weak adaptabilities for the model parameters variation and changing in operating conditions. In order to achieve adaptive controller, the Neural Network (NN) self-tuning PID control is proposed in this paper which combines conventional PID controller and Neural Network learning capabilities. The proportional, integral and derivative (KP, KI, KD) gains are self tuned on-line by the NN output which is obtained due to the error value on the desired output of the system under control. The conventional PID controller in the robot manipulator is replaced by NN self tuning PID controller so as to achieve trajectory tracking with minimum steady-state error and improving the dynamic behavior (overshoot). The simulation results showed that the proposed controller has strong self-adaptability over the conventional PID controller.
format	article
author	Saad Zaghlul Saeed Al-Khayyt
author_facet	Saad Zaghlul Saeed Al-Khayyt
author_sort	Saad Zaghlul Saeed Al-Khayyt
title	Tuning PID Controller by Neural Network for Robot Manipulator Trajectory Tracking
title_short	Tuning PID Controller by Neural Network for Robot Manipulator Trajectory Tracking
title_full	Tuning PID Controller by Neural Network for Robot Manipulator Trajectory Tracking
title_fullStr	Tuning PID Controller by Neural Network for Robot Manipulator Trajectory Tracking
title_full_unstemmed	Tuning PID Controller by Neural Network for Robot Manipulator Trajectory Tracking
title_sort	tuning pid controller by neural network for robot manipulator trajectory tracking
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2017
url	https://doaj.org/article/75fa0ccdd2124970914ea9f74862871c
work_keys_str_mv	AT saadzaghlulsaeedalkhayyt tuningpidcontrollerbyneuralnetworkforrobotmanipulatortrajectorytracking
_version_	1718399522288173056

Tuning PID Controller by Neural Network for Robot Manipulator Trajectory Tracking

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