A parameter identification case-study for a dynamical mechanical system using frequency response analysis and a particle swarm algorithm for trajectory optimization

Gollee, Christian; Majschak, Jens‐Peter

doi:10.1016/j.jestch.2020.02.009

Cited by 4 publications

(3 citation statements)

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“…Parameter identification is used in the creation of mathematical models and controllers [23][24][25]. The proper selection of parameter estimation methods for robotized systems directly impacts the accuracy of these systems.…”

Section: Parameters Identification and System Under Studymentioning

confidence: 99%

Evaluation of Parameter Identification of a Real Manipulator Robot

Urrea

Agramonte

2022

Symmetry

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Given the widespread use of the Kalman filter in robotics, an increasing number of researchers devote themselves to its study and application. This work underscores the importance of this filter while analyzing the modifications made to the same to improve its performance and reduce its deficiencies in some fields and presenting some of its applications in robotics. The following methods are presented in this study: least squares (LS), Hopfield Neural Networks (HNN), Extended Kalman filter (EKF), and Unscented Kalman filter (UKF). These methods are used in the parameter identification of a Selective Compliant Assembly Robot Arm (SCARA) robot with 3-Degrees of Freedom (3-DoF) and a clamp at its end. The dynamic model of this robot is obtained and employed to identify its parameters; then, the identification results are compared considering the difference between the obtained parameters and the real values of the robot parameters; in this comparison, the good results yielded by the LS and UKF method stand out. Subsequently, the obtained parameters through each method are validated by measuring different performance indexes—during trajectory tracking—such as: Residual Mean Square Error (RMSE), Integral of the Absolute Error (IAE), and the Integral of the Square Error (ISE). In this way, a comparison of the robot’s performance is possible.

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Section: Parameters Identification and System Under Studymentioning

confidence: 99%

Evaluation of Parameter Identification of a Real Manipulator Robot

Urrea

Agramonte

2022

Symmetry

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“…Gollee and Majschak developed a computer model in order to include the dynamic behavior of the mechanical system in the motion design. For the identification of unknown model parameters, a method based on the process value curve of the motion control system and the measured frame vibration response is proposed [5]. Suárez et al proposed an experimental mobile manipulator, which is assembled on an omnidirectional platform by a torso with two arms and a human-like structure [6].…”

Section: Related Workmentioning

confidence: 99%

Manipulator Mechanical System Design and Operation Algorithm Based on Multisensor Fusion

Jiao

Li²

2022

Mobile Information Systems

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With the advancement of modern technology and the improvement of the level of social productivity, people are paying more and more attention to the reduction of labor intensity and the change of working environment. At the same time, in order to explore the application of multisensor fusion in the design and operation algorithm of manipulator mechanical system, we first introduced the big data fusion, proposed the multisensor data fusion algorithm, and analyzed the gradient descent method, auxiliary filtering method, and Kalman filter. Finally, the rated motion parameters of each axis motor of the manipulator are analyzed, and at the same time, the motion state of each axis motor of the manipulator under nonrated speed is analyzed. The research proposal in this paper is aimed at contributing to improving productivity.

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“…Another common optimization algorithm that has been used for parameter identification is the Particle Swarm Optimization, which is inspired by the social behavior of bird flocks. Improvements to the standard PSO were proposed after its development [28,75,76], however, its standard version is still in use [77]. In the work of [78] the parameters of a Bouc-Wen hysteretic system are identified by two variants of the PSO algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Ensemble Grey and Black-Box System Identification for Friction Models

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A parameter identification case-study for a dynamical mechanical system using frequency response analysis and a particle swarm algorithm for trajectory optimization

Cited by 4 publications

References 1 publication

Evaluation of Parameter Identification of a Real Manipulator Robot

Evaluation of Parameter Identification of a Real Manipulator Robot

Manipulator Mechanical System Design and Operation Algorithm Based on Multisensor Fusion

Ensemble Grey and Black-Box System Identification for Friction Models

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