dynamic model and inverse kinematic identification of a 3-dof manipulator using rlspso

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ID: 151781
2020
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Abstract
This paper presents the identification of the inverse kinematics of a cylindrical manipulator using identification techniques of Least Squares (LS), Recursive Least Square (RLS), and a dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by RLS (RLSPSO). A helical trajectory in the cartesian space is used as input. The dynamic model is found through the Lagrange equation and the motion equations, which are used to calculate the torque values of each joint. The torques are calculated from the values of the inverse kinematics, identified by each algorithm and from the manipulator joint speeds and accelerations. The results obtained for the trajectories, speeds, accelerations, and torques of each joint are compared for each algorithm. The computational costs as well as the Multi-Correlation Coefficient ( R 2 ) are computed. The results demonstrated that the identification accuracy of RLSPSO is better than that of LS and PSO. This paper brings an improvement in RLS because it is a method with high complexity, so the proposed method (hybrid) aims to improve the computational cost and the results of the classic RLS.
Reference Key
batista2020sensorsdynamic Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Josias Batista;Darielson Souza;Laurinda dos Reis;Antônio Barbosa;Rui Araújo
Journal ekonomiczne problemy usług
Year 2020
DOI
10.3390/s20020416
URL
https://www.mdpi.com/1424-8220/20/2/416
Keywords
dynamic model

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