Todas las publicaciones:
2016
Artículos de revista
Parra-Vega, Vicente; García-Rodríguez, Rodolfo; Armendariz, Jorge
Neurofuzzy self-tuning of the dissipation rate gain for model-free force-position exponential tracking of robots Artículo de revista
En: Neurocomputing, vol. 171, pp. 209 - 219, 2016, ISSN: 0925-2312.
@article{ParraVega2016209,
title = {Neurofuzzy self-tuning of the dissipation rate gain for model-free force-position exponential tracking of robots},
author = {Parra-Vega, Vicente and Garc\'{i}a-Rodr\'{i}guez, Rodolfo and Armendariz, Jorge },
url = {http://www.sciencedirect.com/science/article/pii/S0925231215008590},
doi = {https://doi.org/10.1016/j.neucom.2015.06.034},
issn = {0925-2312},
year = {2016},
date = {2016-01-01},
journal = {Neurocomputing},
volume = {171},
pages = {209 - 219},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2014
Artículos de revista
Armendariz, Jorge; Parra-Vega, Vicente; Garcia-Rodriguez, Rodolfo; Rosales, Sergio
Neuro-fuzzy Self-tuning of PID Control for Semiglobal Exponential Tracking of Robot Arms Artículo de revista
En: Appl. Soft Comput., vol. 25, no C, pp. 139–148, 2014, ISSN: 1568-4946.
@article{Armendariz:2014:NSP:2842034.2842100,
title = {Neuro-fuzzy Self-tuning of PID Control for Semiglobal Exponential Tracking of Robot Arms},
author = { Armendariz, Jorge and Parra-Vega, Vicente and Garcia-Rodriguez, Rodolfo and Rosales, Sergio },
url = {http://dx.doi.org/10.1016/j.asoc.2014.08.037},
doi = {10.1016/j.asoc.2014.08.037},
issn = {1568-4946},
year = {2014},
date = {2014-01-01},
journal = {Appl. Soft Comput.},
volume = {25},
number = {C},
pages = {139--148},
publisher = {Elsevier Science Publishers B. V.},
address = {Amsterdam, The Netherlands, The Netherlands},
abstract = {Abstract
The PID controller with constant feedback gains has withstood as the preferred choice for control of linear plants or linearized plants, and under certain conditions for non-linear ones, where the control of robotic arms excels. In this paper a model-free self-tuning PID controller is proposed for tracking tasks. The key idea is to exploit the passivity-based formulation for robotic arms in order to shape the damping injection to enforce dissipativity and to guarantee semiglobal exponential convergence in the sense of Lyapunov. It is shown that a neuro-fuzzy network can be used to tune dissipation rate gain through a self-tuning policy of a single gain. Experimental studies are presented to confirm the viability of the proposed approach.
Graphical abstract},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract
The PID controller with constant feedback gains has withstood as the preferred choice for control of linear plants or linearized plants, and under certain conditions for non-linear ones, where the control of robotic arms excels. In this paper a model-free self-tuning PID controller is proposed for tracking tasks. The key idea is to exploit the passivity-based formulation for robotic arms in order to shape the damping injection to enforce dissipativity and to guarantee semiglobal exponential convergence in the sense of Lyapunov. It is shown that a neuro-fuzzy network can be used to tune dissipation rate gain through a self-tuning policy of a single gain. Experimental studies are presented to confirm the viability of the proposed approach.
Graphical abstract
