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© Thomson Reuters, Journal Citation Reports, 2016

Indexada en:

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Métricas

  • Factor de Impacto: 0,500(2016)
  • 5-años Factor de Impacto: 0,344
  • SCImago Journal Rank (SJR):0,212
  • Source Normalized Impact per Paper (SNIP):0,308

© Thomson Reuters, Journal Citation Reports, 2016

Revista Iberoamericana de Automática e Informática industrial 2017;14:141-51 - DOI: 10.1016/j.riai.2017.01.001
Control Neuronal en Línea para Regulación y Seguimiento de Trayectorias de Posición para un Quadrotor
On Line Adaptive Neurocontroller for Regulating Angular Position and Trajectory of Quadrotor System
Hugo Yañez-Badilloa, , Ruben Tapia-Olverab,, , Omar Aguilar-Mejíaa, , Francisco Beltran-Carbajalc,
a Departamento de posgrado, Universidad Politécnica de Tulancingo, Ingenierías #100, Col. Huapalcalco, 43629, Tulancingo, Hgo., México
b Departamento de Ingeniería Eléctrica, Universidad Nacional Autónoma de México, Av. Universidad 3000, Cd. Universitaria, Delegación Coyoacán, 04510 Ciudad de México, México
c Departamento de Energía, Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Av. San Pablo 180, Col. Reynosa Tamaulipas, Delegación Azcapotzalco, 02200 Ciudad de México, México
Resumen

Los sistemas de control automático día a día se han convertido en elementos importantes en la vida cotidiana, en tal sentido, se deben proponer nuevas y mejores formas de incorporar modelos matemáticos y algoritmos de control adaptativos para superar la gran cantidad de cambios técnicos y físicos a los que se enfrentan para su utilización. En este artículo se realiza el control de posición y seguimiento de trayectorias para un Quadrotor. Debido a la naturaleza no lineal de este sistema subactuado, se propone el empleo de un controlador adaptativo basado en redes neuronales B-spline que permita determinar las señales de control mediante un entrenamiento dividido en dos etapas: a) uno inicial fuera de línea y; b) uno continuo en línea. Esta forma de aprendizaje permite al Quadrotor extender un desempeño satisfactorio ante diferentes condiciones operativas y seguimiento de los valores de referencia. Los resultados de simulación verifican la aplicabilidad del controlador propuesto y el impacto que se tiene en el desempeño del sistema minimizando la necesidad de contar con un modelo matemático no lineal detallado, así como el conocimiento exacto de los valores de los parámetros del Quadrotor.

Abstract

Automatic control systems every day become more important in everyday life; therefore, it must find new and better ways to incorporate mathematical models and adaptive control algorithms to cope with a number of technical and physical challenges for exploitation. In this paper, the algorithm of the dynamic model of a Quadrotor applied to an angular position and trajectory control as a study case is detailed. Due to nonlinear nature of this type of systems, an adaptive on line neurocontroller algorithm based on B-spline neural networks is proposed, the learning procedure is divided in two stages: a) an initial off line training and; b) an on line continuous learning. This form of learning allows the Quadrotor extend its satisfactory performance at different operating conditions and trajectory tracking. The simulation results demonstrate the applicability of the developed model and the impact of dynamic control on the system performance, diminishing the exact model requirement and the possibility to incorporate the system non linearities.

Palabras clave
Control Neuronal, Control Libre de Modelo, Aprendizaje Automático, Sistemas Subactuados
Keywords
Neural Network Control, Model-Free Control, Automatic Learning, Underactuated Systems
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Autor para correspondencia. (Ruben Tapia-Olvera rtapia@fi-b.unam.mx)
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