Experimental verification of an online traction parameter identification method

Kobelski, Alexander; Osinenko, Pavel; Streif, Stefan

doi:10.1016/j.conengprac.2021.104837

Cited by 3 publications

(1 citation statement)

References 20 publications

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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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A suboptimal control scheme based on integrated real-time nonlinear estimation-identification approach for developed flexible joint manipulators structure

Korayem,

Lademakhi

2024

Control Engineering Practice

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Estimation of vehicle tire effective rolling radius and vertical wheel/road contact force

Rabhi,

Hartani,

Guettaf

2024

Proceedings of the Institution of Mechanical Engineers, Part D:

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In a vehicle, knowledge of physical wheel/road contact parameters is valuable for the design of dynamic control and active safety systems. In this paper, a new method is designed to estimate the effective rolling radius and the vertical wheel/road contact force using an extended Kalman filter and the Pacejka magic formula. Moreover, it allows the estimation of the longitudinal force, the rolling resistance force, and the longitudinal slip. The method is designed in four blocks. First, a comparison with other research is carried out to validate both the controller and quarter-vehicle model blocks. Then, the blocks of the extended Kalman filter and the Pacejka magic formula are applied to estimate all parameters. The relative mean estimation errors are calculated, and the results show that the developed method is capable of estimating all parameters simultaneously and in real-time with satisfactory accuracy.

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Experimental verification of an online traction parameter identification method

Cited by 3 publications

References 20 publications

Evaluation of Parameter Identification of a Real Manipulator Robot

Evaluation of Parameter Identification of a Real Manipulator Robot

A suboptimal control scheme based on integrated real-time nonlinear estimation-identification approach for developed flexible joint manipulators structure

Estimation of vehicle tire effective rolling radius and vertical wheel/road contact force

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