contributor author | T. Efrati | |
contributor author | H. Flashner | |
date accessioned | 2017-05-08T23:59:18Z | |
date available | 2017-05-08T23:59:18Z | |
date copyright | March, 1999 | |
date issued | 1999 | |
identifier issn | 0022-0434 | |
identifier other | JDSMAA-26252#148_1.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/121958 | |
description abstract | A method for tracking control of mechanical systems based on artificial neural networks is presented. The controller consists of a proportional plus derivative controller and a two-layer feedforward neural network. It is shown that the tracking error of the closed-loop system goes to zero while the control effort is minimized. Tuning of the neural network’s weights is formulated in terms of a constrained optimization problem. The resulting algorithm has a simple structure and requires a very modest computation effort. In addition, the neural network’s learning procedure is implemented on-line. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Neural Network Based Tracking Control of Mechanical Systems | |
type | Journal Paper | |
journal volume | 121 | |
journal issue | 1 | |
journal title | Journal of Dynamic Systems, Measurement, and Control | |
identifier doi | 10.1115/1.2802435 | |
journal fristpage | 148 | |
journal lastpage | 154 | |
identifier eissn | 1528-9028 | |
keywords | Artificial neural networks | |
keywords | Tracking control | |
keywords | Control equipment | |
keywords | Algorithms | |
keywords | Optimization | |
keywords | Closed loop systems | |
keywords | Computation | |
keywords | Errors AND Feedforward control | |
tree | Journal of Dynamic Systems, Measurement, and Control:;1999:;volume( 121 ):;issue: 001 | |
contenttype | Fulltext | |