| Titre : | Position control of servomotors using neural dynamic sliding mode (2012) |
| Auteurs : | A. Karami-Mollaee, Auteur ; N. Pariz, Auteur ; H. M. Shanechi, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Transactions of the ASME . Journal of dynamic systems, measurement, and control (Vol. 133 N° 6, Novembre 2011) |
| Article en page(s) : | 10 p. |
| Note générale : | Dynamic systems |
| Langues : | Anglais |
| Index. décimale : | 553 (Géologie économique. Minérographie. Minéraux. Formation et gisements de minerais) |
| Tags : | Adaptive control Learning (artificial intelligence) Machine Neurocontrollers Nonlinear systems PI Position Radial basis function networks Robust Servomotors State feedback Variable structure |
| Résumé : | In this paper, position control of servomotors is addressed. A radial basis function neural network is employed to identify the unknown nonlinear function of the plant model, and then a robust adaptive law is developed to train the parameters of the neural network, which does not require any preliminary off-line weight learning. Moreover, base on the identified model, we propose a new dynamic sliding mode control (DSMC) for a general class of nonaffine nonlinear systems by defining a new adaptive proportional-integral sliding surface and employing a linear state feedback. The main property of proposed controller is that it does not need an upper bound for the uncertainty and identified model; moreover, the switching gain increases and decreases according to the system circumstance by employing an adaptive procedure. Then, chattering is removed completely by using the DSMC with a small switching gain. |
| DEWEY : | 553 |
| ISSN : | 0022-0434 |
| En ligne : | http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JDSMAA000133000006061014000001&idtype=cvips&gifs=Yes&ref=no |
