Please use this identifier to cite or link to this item:
|Title:||Continuous-time neural control for a 2 DOF vertical robot manipulator|
|Abstract:||A diode-pumped erbium-doped fiber laser with pump modulation shows a large dynamic behavior including the coexistence of multiple attractors. In this work it is demonstrated numerically and experimentally that a low-pass noise filtering can control the probability for the appearance of a particular state. The results of numerical simulations with the use of a three-level laser model display good agreement with experimental results. We construct three-dimensional bifurcation diagrams of the probability using the noise amplitude and cutting frequency as control parameters. Finally, it is found the existence of one noise amplitude that cause one periodic orbit to appears more frequently compared to different noise amplitudes. " 2012 IFAC.",,,,,,"10.3182/20120620-3-MX-3012.00059",,,"http://hdl.handle.net/20.500.12104/40351","http://www.scopus.com/inward/record.url?eid=2-s2.0-84880989617&partnerID=40&md5=3c962410c6f296acf4178fa4752e47a8",,,,,,,,"IFAC Proceedings Volumes (IFAC-PapersOnline)",,"232|
236",,,,"Scopus",,,,,,"Control; Fiber laser; Multistable; Noise; Non-linear; Optical fiber",,,,,,"Control of attractor preference by low-pass filtered noise in a multistable fiber laser",,"Conference Paper" "42114","123456789/35008",,"Jurado, F., Instituto Tecnológico de la Laguna, Torreon, Coahuila de Zaragoza, Mexico, 27000, Mexico; Flores, M.A., Instituto Tecnológico de la Laguna, Torreon, Coahuila de Zaragoza, Mexico, 27000, Mexico; Castañeda, C.E., Universidad de Guadalajara, Centro Universitario de los Lagos, Lagos de Moreno, Jalisco, México, 47460, Mexico",,"Jurado, F.
Castaneda, C.E.",,"2011",,"This paper presents a continuous-time neural control scheme for identification and control of a two degrees of freedom (DOF) direct drive vertical robot manipulator model, on which effects due to friction and gravitational forces are both considered. A recurrent high-order neural network (RHONN) structure is proposed in order to identify the plant model to then, based on this neural structure, derive a neural controller using the backstepping design methodology. The trajectory tracking performance of the neural controller is illustrated via simulations results, which suggest the validity of the proposed approach for its implementation in real-time. " 2011 IEEE.
|Appears in Collections:||Producción científica UdeG|
Files in This Item:
There are no files associated with this item.
Items in RIUdeG are protected by copyright, with all rights reserved, unless otherwise indicated.