Dynamic Parameter Identification Based on Lagrangian Formulation and Servomotor-type Actuators for Industrial Robots

Urrea, Claudio; Pascal, José

doi:10.1007/s12555-020-0476-8

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…The original data dimension of the workload in the identification experiment is [9,2500,18]. There are 9 groups of different load values, and each group of load experiments produces 2500 × 18 data points.…”

Section: Parameter Analysis Of Prediction Modelmentioning

confidence: 99%

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A workload identification method of industrial robot combining dynamic model and convolutional neural network

Yue,

Wang,

Zhang

et al. 2024

Eng. Res. Express

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The motion joint system of industrial robot has obvious nonlinear characteristics, high dimensional running data and limited number of experimental samples. If mature neural network algorithm is used for identifying the workload of robots, it is easy to have over-learning problems, which seriously restricts the generalization ability of load identification method. In this paper, a load identification method combining the dynamic model of industrial robots with the neural network data model is proposed. A UR5 robot is used for carrying out multiple dynamic load testings to verify the effectiveness of the proposed identification method. Firstly, the workload identification by CNN algorithm is given, and the influence of parameters of prediction model and different neutral network are analyzed. Then the classical dynamic model of industrial robots with multi-degrees of freedom is established. The identified workload by dynamic model is also analyzed. At last, the deterministic information such as velocity and displacement is extracted from the calculation results of the dynamic model as the initial anchoring value. Then convolutional neural network is applied for compensating the residual highly nonlinear information. An improved mixing combination method is also proprosed. This method can effectively deal with the interaction of different types of information in the data, and preliminarily cooperate the dynamic model and convolutional neural network. This provides a basic framework and method for solving the problem of parameter identification in multi-degree-of-freedom systems with small samples.

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Section: Parameter Analysis Of Prediction Modelmentioning

confidence: 99%

“…Therefore the numerical methods are used for analysis. But the efficiency of the numerical method is low under the condition of large excitation variation and complex working conditions [18]. In addition, the frictional damping will drift with the running time of the system with a certain extent.…”

Section: Introduction 1motivationsmentioning

confidence: 99%

A workload identification method of industrial robot combining dynamic model and convolutional neural network

Yue,

Wang,

Zhang

et al. 2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…This formulation describes the behavior of a dynamic system in terms of work and energy stored in it. The closed-form equations can be systematically derived in any coordinate system [33].…”

Section: Lagrange-euler Formulationmentioning

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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“…In the case of higher degrees of freedom, it is cumbersome to identify the basic parameters by this method. In Urrea C' s study [18], dynamic model was determined by the information of acceleration, velocity, position and voltage. Therefore, it does not calculate the joint torque.…”

Section: Introductionmentioning

confidence: 99%

Parameters identification and contact interaction control of redundant robot based on dynamic model

Li,

Bian,

Wang

2024

Phys. Scr.

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Human-redundant robot contact interaction control based on joint current is conducted. This is a sensorless control method. The key issues involved are dynamic parameters identification and interaction control strategy of robot. Firstly, kinematics model and linear dynamic model of robot are established and dynamic parameter matrix to be identified is obtained. Pattern search method is adopted to optimize motion trajectory of robot. The least square method is used to obtain dynamic parameters. The results indicate that the calculated current has a consistent trend with actual measured value, and the current error is small. In order to achieve independent control in null space, motion decoupling model of robot is established, and impedance control is applied to null space. When the force is applied to robot, based on impedance control and joint current changes, robot can move along the force direction, so the motion in null space can be controlled. Aiming at null space contact control during end force control task, comprehensive interaction control based on force sensor and joint current is conducted. The results show that robot can also move in compliance with human hand and achieve compliant interaction of null space when force is applied to the joint space.

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Dynamic Parameter Identification Based on Lagrangian Formulation and Servomotor-type Actuators for Industrial Robots

Cited by 9 publications

References 22 publications

A workload identification method of industrial robot combining dynamic model and convolutional neural network

A workload identification method of industrial robot combining dynamic model and convolutional neural network

Evaluation of Parameter Identification of a Real Manipulator Robot

Parameters identification and contact interaction control of redundant robot based on dynamic model

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