Identificación paramétrica del modelo NARMAX del comportamiento dinámico de la posición del efector final de un manipulador robótico de tres grados de libertad.

• Autor: Mozo Castillo, Cristhian Joel
• Formato: tesis de grado
• Fecha de Publicación: 2023
• Institución: Universidad Nacional de Trujillo
• Repositorio: UNITRU-Tesis
• Lenguaje: español
• OAI Identifier: oai:dspace.unitru.edu.pe:20.500.14414/16459
• Enlace del recurso: https://hdl.handle.net/20.500.14414/16459
• Nivel de acceso: acceso abierto
• Materia: posición del efector final de un manipulador robótico de tres grados de libertad.

The development of this thesis work consists on obtaining the NARMAX model of the dynamic behavior of the end-effector position of a robotic manipulator of three degrees of freedom by means of a parametric identification process. for this experimental data of the angular position of each of the joints is obtained after being excited with a certain signal, from the angular position data and the direct kinematic we obteined the end-effector position of the robotic manipulator, after obtaining the experimental data, we proceeded to select the elements of the structure of our parametric model, once the elements of the structure of the model have been selected, the coefficients of these elements were estimated to end with the validation of the model obtained. For the execution of the experimental tests a robotic manipulator of three degrees of freedom was implemented, which is made up of three servomotors that function as actuators of each one of the joints of the robotic manipulator, the internal potentiometers of each of the servomotors were used as position sensor, and the base material used for the construction of the links is aluminum, for the acquisition of data, an Arduino Leonardo board was used in communication with a laptop with Matlab software. For the selection of the elements of the model structure, the error reduction ratio (ERR) criterion and the FROLS algorithm were used to determine the most relevant elements of all the candidate elements, the parameter estimation process was done with the recursiveleast square (RLS) algorithm commonly used in adaptive filters, to finalize, for the validation of the model, the criterion of the mean square error was used.