• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.7
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• pp.765-772
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• 1990

A practical method of identifying the inertial parameters, viscous friction and Coulomb friction of a robot is presented. The parameters in the dynamic equations of a robot are obtained from the measurements of the command voltage and the joint position of the robot. First, a dynamic model of the integrated system of the mainpulator and motor is derived. An off-line parameter identification procedure is developed and applied to the University of Minnesota Direct Drive Robot. To evaluate the accuracy of the parameters the dynamic tracking of the robot was tested. The trajectroy errors were significantly reduced when the identified dynamic parameters were used.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.705-710
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• 1989

A practical method of identifying the inertial parameters, viscous friction and Coulomb friction of a robot is presented. The parameters in the dynamic equations of a robot are obtained from the measurements of the command voltage and the joint position of the robot. First, a dynamic model of the integrated motor and manipulator is derived. An off line parameter identification procedure is developed and applied to the University of Minnesota Direct Drive Robot. To evaluate the accuracy of the parameters the dynamic tracking of robot was tested. The trajectory errors were significantly reduced when the identified dynamic parameters were used.

• Smart Structures and Systems
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• v.15 no.1
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• pp.169-189
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• 2015

Convergence difficulty and available complete measurement information have been considered as two primary challenges for the identification of large-scale engineering structures. In this paper, a time domain substructural identification approach by combining a weighted adaptive iteration (WAI) algorithm and an extended Kalman filter method with a weighted global iteration (EFK-WGI) algorithm was proposed for simultaneous identification of physical parameters of concerned substructures and unknown external excitations applied on it with limited response measurements. In the proposed approach, according to the location of the unknown dynamic loadings and the partially available structural response measurements, part of structural parameters of the concerned substructure and the unknown loadings were first identified with the WAI approach. The remaining physical parameters of the concerned substructure were then determined by EFK-WGI basing on the previously identified loadings and substructural parameters. The efficiency and accuracy of the proposed approach was demonstrated via a 20-story shear building structure and 23 degrees of freedom (DOFs) planar truss model with unknown external excitation and limited observations. Results show that the proposed approach is capable of satisfactorily identifying both the substructural parameters and unknown loading within limited iterations when both the excitation and dynamic response are partially unknown.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10a
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• pp.932-938
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• 1987

The dynamic equations of robotic manipulators can be derived from either Newton-Euler equation or Lagrangian equation. Model parameters which appear in the resulting dynamic equation are the nonlinear functions of both the inertial parameters and the geometric parameters of robotic manipulators. The identification of the model parameters is important for advanced robot control. In the previous methods for the identification of the model parameters, the geometric parameters are required to be predetermined, or the robotic manipulators are required to follow some special motions. In this paper, we propose an approach to the identification of the model parameters, in which prior knowledge of the geometric parameters is not necessary. We show that the estimation equation for the model parameters can be formulated in an upper block triangular form. Utilizing the special structures, we obtain a simplified least-square estimation algorithm for the model parameter identification. To illustrate the practical use of our method, a 4DOF SCARA robot is examined.

• The Journal of Korea Robotics Society
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• v.7 no.3
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• pp.222-230
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• 2012

The paper presents a momentum-based regressor by using Hamiltonian dynamics representation for robotic manipulator. It has an advantage in that the proposed regressor does not require the acceleration measurement for the identification of dynamic parameters. Also, the identification algorithm is newly suggested by solving a minimization problem with constraint. The developed algorithm is easy to implement in real-time. Finally, the effectiveness of the proposed momentum-based regressor and identification method is shown through numerical simulations.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.852-856
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• 2004

An induction motor is one of the most popular electrical apparatuses owing to its simple structure and robust construction. Parameter identification of the induction motor has long been researched either for a vector control technique or fault detection. Since vector control is a well-established technique for induction motor control, this paper concentrates on successive identification of physical parameters with on-load data for the purpose of condition monitoring and/or fault detection. For extracting six physical parameters from the on-load data in the framework of the induction motor state equation, unmeasured initial state values and profiles of load torque have to be estimated as well. However, the analytic optimization methods in general fail to estimate these auxiliary but significant parameters owing to the difficulty of obtaining their gradient information. In this paper, the univariate dynamic encoding algorithm for searches (uDEAS) newly developed is applied to the identification of whole unknown parameters in the mathematical equations of an induction motor with normal operating data. Profiles of identified parameters appear to be reasonable and therefore the proposed approach is available for fault diagnosis of induction motors by monitoring physical parameters.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.2
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• pp.128-133
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• 2010

This paper presents a method for estimating the parameters of dynamic models for induction motor dominating loads. Using particle swarm optimization, the method finds the adequate set of parameters that best fit the sampling data from the measurement for a period of time, minimizing the error of the outputs, active and reactive power demands and satisfying the steady-state error criterion.

• Structural Engineering and Mechanics
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• v.84 no.3
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• pp.413-421
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• 2022

Modal parameters are the main dynamic characteristics of bridge. This study aims to propose an innovative route to estimate the modal parameters for bridges by using a parked vehicle in which mode shapes with high accuracy and spatial resolution are identified by frequency measurement. Based on the theory of dynamic modification and modal identification, the mathematical formulation between the parked mass induced frequency variation and the modal parameters of a bridge is derived. Then this mathematical formulation is extended to a parked vehicle-bridge system. The arithmetic and processes for estimating the modal parameters based on the identified frequency variation of the vehicle-bridge systems when the vehicle locates at sequentially arranged positions are presented. Finally the proposed method is applied to several simulated bridges of different types. The results indicate that it can estimate the modal parameters with high accuracy and efficiency.

• Structural Engineering and Mechanics
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• v.88 no.4
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• pp.301-309
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• 2023

In this paper, a new modified conjugate gradient (MCG) method is presented which is based on a new gradient regularizer, and this method is used to identify the dynamic load on airfoil structure without and with considering random structure parameters. First of all, the newly proposed algorithm is proved to be efficient and convergent through the rigorous mathematics theory and the numerical results of determinate dynamic load identification. Secondly, using the perturbation method, we transform uncertain inverse problem about force reconstruction into determinate load identification problem. Lastly, the statistical characteristics of identified load are evaluated by statistical methods. Especially, this newly proposed approach has successfully solved determinate and uncertain inverse problems about dynamic load identification. Numerical simulations validate that the newly developed method in this paper is feasible and stable in solving load identification problems without and with considering random structure parameters. Additionally, it also shows that most of the observation error of the proposed algorithm in solving dynamic load identification of deterministic and random structure is respectively within 11.13%, 20%.

• Journal of Power Electronics
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• v.10 no.1
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• pp.58-64
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• 2010

In this paper, the dynamic performance of oscillating linear motors, which are used in household refrigerators, is improved by means of efficient parameter identification. Oscillating linear motor parameters are identified as a function of the piston position and the motor current. They are stored in a ROM table and used later for an accurate estimation of piston position. The identified motor parameters are also approximated to the $2^{nd}$-order surface functions, which are divided into 2 or 4 subsections in order to decrease identification errors. Experimental results are given to show that the proposed control scheme can provide oscillating linear motors with high dynamic performance.