Metaheuristic Identification for an Analytic Dynamic Model of a Delta Robot with Experimental Verification

Tae-Hyoung, Kim; Kim, Yeongjae; Kwak, Taeheon; Kanno, Masaaki

doi:10.3390/act11120352

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…The proposed approach can compensate for extrinsic mass effect and identify the changed model under insufficient excitations. Kim proposed an efficient system-identification method by the principle of virtual work for a delta robot [28]. The dynamic-parameter identification technique is presented to estimate a set of uncertain parameters in the extended dynamic model.…”

Section: Literature Reviews and Objectivesmentioning

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

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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: Literature Reviews and Objectivesmentioning

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

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show abstract

“…Due to the lightweight structure of the arms and struts, the slender struts (also called 'forearms') are often regarded as point-masses located at the end of the upper arm [6], [19]. In [14], [20], [21] half of the mass of a strut is added to the end of the upper arm and to the moving platform, respectively. In [19] it is assumed that the center of mass (COM) of the upper arm is at the geometric center.…”

Section: B Model Simplificationmentioning

confidence: 99%

“…However, none of these account for physical consistency of the inertia and friction parameters. Additionally, [27], [20] further rely on CAD data available for the identification scheme.…”

Section: B Model Simplificationmentioning

confidence: 99%

Dedicated Dynamic Parameter Identification for Delta-Like Robots

Gnad,

Gattringer,

Müller

et al. 2024

IEEE Robot. Autom. Lett.

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Dynamics simulation of parallel kinematic manipulators (PKM) and non-linear control methods require a precisely identified dynamics model and explicit generalized mass matrix. Standard methods, which identify so-called dynamic baseparameters, are not sufficient to this end. Algorithms for identifying the complete set of dynamic parameters were proposed for serial manipulators. A dedicated identification method for PKM does not exist, however. Such a method is introduced here for the large class of Delta-like PKM exploiting the parallel structure and making use of model simplifications specific to this class. The proposed method guarantees physical consistency of the identified parameters, and in particular a positive definite generalized mass matrix. The method is applied to a simulated model with exactly known parameters, which allows for verification of the obtained dynamic parameters. The results show that the generalized mass matrix, the acceleration, and the Coriolis, gravitation and friction terms in the equations of motion (EOM) are well approximated. The second example is a real 4-DOF industrial Delta robot ABB IRB 360-6/1600. For this robot, a physically consistent set of inertia and friction parameters is identified from measurements. The method allows prescribing estimated parameters, but does not rely on such data, e.g. from manufacturer or CAD.

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“…The created method offers a novel method for controlling the inverse kinematics of a delta robot. The approach for system identification for a delta robot is suggested in [3]. The dynamic behavior of the parallel-link robot was theoretically modeled using the virtual work concept.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Inverse Dynamic Deep Neural Network Tracking Control for Delta Robot Based on a COVID-19 Optimization

Shamseldin

2023

Journal of Robotics and Control

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This paper presents a new technique to design an inverse dynamic model for a delta robot experimental setup to obtain an accurate trajectory. The input/output data were collected using an NI DAQ card where the input is the random angles profile for the three-axis and the output is the corresponding measured torques. The inverse dynamic model was developed based on the deep neural network (NN) and the new COVID-19 optimization to find the optimal initial weights and bias values of the NN model. Due to the system uncertainty and nonlinearity, the inverse dynamic model is not enough to track accurately the preselected profile. So, the PD compensator is used to absorb the error deviation of the end effector. The experimental results show that the proposed inverse dynamic deep NN with PD compensator achieves good performance and high tracking accuracy. The suggested control was examined using two different methods. The spiral path is the first, with a root mean square error of 0.00258 m, while the parabola path is the second, with a root mean square error of 0.00152 m.

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Metaheuristic Identification for an Analytic Dynamic Model of a Delta Robot with Experimental Verification

Cited by 6 publications

References 15 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

Dedicated Dynamic Parameter Identification for Delta-Like Robots

Real-Time Inverse Dynamic Deep Neural Network Tracking Control for Delta Robot Based on a COVID-19 Optimization

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