• Proceedings of the KIPE Conference
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• 2008.06a
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• pp.301-303
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• 2008

Tuning of the speed controller in the linear servo applications needs the accurate information of a mover mass including a load mass. Therefore this paper proposes the mass estimation method of a permanent magnet linear synchronous motor(PMLSM) applied at the vertical axis by using the recursive Least-Squares estimation algorithm. First, this paper derives the deterministic autoregressive moving average(DARMA) model of the mechanical dynamic system used at the vertical axis. Results obtained by the Matlab/Simulink simulation show that the mass of a PMLSM applied at the vertical axis can be accurately estimated both at no-load and at load.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.192-199
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• 2002

This paper demonstrates that hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system Identification of electro-hydraulic servo system. This algorithm are consist of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. We manufactured electro-hydraulic servo system and the hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values(mass, damping coefficient, bulk modulus, spring coefficient) which minimize total square error.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.442-447
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• 2003

This paper demonstrates that a modified hybrid neural-genetic multimodel parameter estimation algorithm can be applied to structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. The modified hybrid neural-genetic multimodel parameter estimation algorithm is applied to an electro-hydraulic servo system the task to find the parameter values such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimizes the total square error.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.5
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• pp.196-203
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• 2003

This paper demonstrates that an improved hybrid neural-genetic multimodel parameter estimation algorithm can be applied to the structured system identification of an electro-hydraulic servo system. This algorithm is consists of a recurrent incremental credit assignment (ICRA) neural network and a genetic algorithm, The ICRA neural network evaluates each member of a generation of model and the genetic algorithm produces new generation of model. We manufactured an electro-hydraulic servo system and the improved hybrid neural-genetic multimodel parameter estimation algorithm is applied to the task to find the parameter values, such as mass, damping coefficient, bulk modulus, spring coefficient and disturbance, which minimize total square error.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.4
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• pp.62-72
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• 2013

To operate an automatic picking system in distribution center with high precision and high dynamics, this paper presents a robust control scheme of a linear induction motor (LIM) using the mass estimation and disturbance force observer. The force disturbance which gives a direct influence on the control performance of LIM is estimated in real-time through the disturbance observer and compensated by a feedforward manner. To get a satisfactory performance even under the mass variation by reducing the disturbance force due to the mismatched mass during the speed transient such as the acceleration and deceleration periods, a mass estimation algorithm is proposed. A Simulink model for LIM is developed and the validity of the proposed scheme is verified through the comparative simulation studies using Matlab - Simulink.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1199-1209
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• 2021

The detection of unexpected loose parts in the primary coolant system in a nuclear power plant remains an extremely important issue. It is essential to develop a methodology for the localization and mass estimation of loose parts owing to the high prediction error of conventional methods. An effective approach is presented for the localization and mass estimation of a loose part using machine-learning and deep-learning algorithms. First, a methodology was developed to estimate both the impact location and the mass of a loose part at the same times in a real structure in which geometric changes exist. Second, an impact database was constructed through a series of impact finite-element analyses (FEAs). Then, impact parameter prediction modes were generated for localization and mass estimation of a simulated metallic loose part using machine-learning algorithms (artificial neural network, Gaussian process, and support vector machine) and a deep-learning algorithm (convolutional neural network). The usefulness of the methodology was validated through blind tests, and the noise effect of the training data was also investigated. The high performance obtained in this study shows that the proposed methodology using an FEA-based database and deep learning is useful for localization and mass estimation of loose parts on site.

• Tunnel and Underground Space
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• v.15 no.2 s.55
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• pp.129-136
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• 2005

This paper describes the implementation of rock mass rating evaluation based on genetic algorithm(GA) and conditional simulation technique to estimate RMR in the area without sufficient borehole data RMR were estimated by GA and conditional simulation technique with reflecting distribution feature and spatial correlation. And RMR determined by GA were compared with the results from kriging. Through the analysis of the results from 30 simulations, the uncertainty of estimation could be quantified.

• The Transactions of the Korean Institute of Power Electronics
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• v.5 no.6
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• pp.575-583
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• 2000

In this paper, a novel estimation alogrithm of mechanical parameters in two-mass system proposed. The inertia of a load and a motor and the stiffness are estimated by using RLS(Recursive Least Square) algorithm and acceleration information of motor. The effectiveness of the proposed scheme is verified with simulation and experiments results.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.274-277
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• 1999

This paper is concerned with estimating the mass of a loose part in the steam generator of a nuclear power plant. Although there is the basic principle known as “Hertz Theory”for estimating mass and energy of a spherical part impacted on an infinite flat plate, the theory is not directly applicable because real plants do not comply with the underlying ideal assumptions. (Say, the steam generator is of a cylindrical and hemisphere shape.) In this work, a practical method is developed based on the basic theory and considering amplitude and energy attenuation effects. Actually, the impact waves propagating along the plate to the sensor locations become significantly different in shape and frequency spectrum from the original waveform due to the plate and surrounding conditions, distance attenuation and damping loss. To show the validity of the present mass estimation algorithm, it has been applied to the mock-up impact test data and also to real plant data. The results show better performance comparing to the conventional Hertz schemes.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.307-311
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• 2003

2-mass system is generally used as controller of the variable-speed to transfer electromotion power to mechanical load such as industrial robot, driving parts of electric vehicle, rolling machine system of steel plant and driving parts of elevator. In this case, PI controller is often used as a velocity controller because of simplicity of system. But PI control algorithm is not enough for obtaining the control characteristics required for this system. To solve this problem, 2-mass system based on the PID controller derives the optimum PID parameters by pole assignment and estimation of the ITAE performance index. In this case, the system have tenacious properties about disturbance, but it causes extreme overshoot and vibration because of rapidly output of controller in early transient response about desired value. And if speed control system is applied by 2-DOF parameter ${\alpha}$, a temporary value, we must induce most suitable parameter by complicate pole assignment and estimation of the ITAE performance index whenever ${\alpha}$ changes. In this paper, to solve this problem we suggest control algorithm to followed exactly value of ${\alpha}$ as 2-DOF parameter by using fuzzy algorithm . So, intelligence algorithm modeled by human knowledge, experience, teachability and judgment follow exact ${\alpha}$ value and it can compose the efficient 2-DOF PID controller to improve following performance, overshoot decrease.