• Journal of the Semiconductor & Display Technology
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• v.23 no.2
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• pp.92-99
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• 2024

As semiconductor processes require several nanometers precision, the importance of motor control is increasing in semiconductor equipment. Due to unpredictable uncertainties such as friction and mechanical vibrations achieving precise position control in semiconductor processes is challenging. The internal model principle-based controller is a control technique that ensures robust steady-state performance by incorporating a model of the reference and disturbance. The disturbance observer-based controller is a prominent robust control technique implemented to cope with various nonlinearities and uncertainties. Provided that the two controllers can be designed to exhibit equivalent performance under certain conditions, this paper demonstrates through experiments that they yield identical results for the case of a BLDC position control problem. The experimental results also indicate that they can offer enhanced robustness compared with the conventional PID controller in the presence of a time-varying disturbance.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.372-378
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• 2007

This paper discusses Robust $H{\infty}$ control problems for networked control systems (NCSs) with time delays and subject to norm-bounded parameter uncertainties. Based on a new discrete-time model, two approaches of robust controller design are proposed. A numerical example and experimental verification with an NCS test bed are given to illustrate the feasibility and effectiveness of proposed design methodologies.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2783-2786
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• 2003

In this paper, the speed controller of permanent-magnet synchronous motor (PMSM) using the RBF neural (NN) disturbance observer is proposed. The suggested controller is designed using the input-output feedback linearization technique for the nominal model of PMSM and incorporates the RBF NN disturbance observer to compensate for the system uncertainties. Therefore the proposed algorithm is robust against the uncertainties of the system. Finally, the computer simulation is rallied out to verify the effectiveness of the proposed method.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.3
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• pp.90-101
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• 2000

In this paper, we propose a controller for differentially steered wheeled mobile robots. The controller uses input-output linearization algorithm and artificial neural network to stabilize the dynamic model and compensate uncertainties. The proposed neural network part has 6 inputs, 1 hidden layer, 2 torque outputs and features fast online learning and good performance on structure error learning basis. Simulation results show that the proposed controller perform precisely tracking of reference path and is robust to uncertainties.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.60-65
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• 1991

In this paper, robust controllers which guarantee the stability and the quadratic performance in the presence of the state and the input matrix uncertainties are presented. Modified quadratic performance indices which include the model uncertainties are proposed for continuous and discrete time linear systems. And it is shown that the solution of the proposed optimal performance problem is the robust controller.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.687-693
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• 1989

A robust deterministic control for a class of singularly perturbed uncertain systems, where uncertainties are characterized deterministically rather than stochastically, is developed based mainly on information available on an uncertain reduced-order system. The deterministic control scheme is applied to the motion control of a n degree of freedom robotic manipulator. The parasitic actuator and sensor dynamics of the manipulator are explicitly considered in the stability analysis of the deterministic controller using a singular perturbation model. Simulation and experimental studies for a two degree of freedom, direct drive SCARA manipulator are conducted to evaluate the effectiveness of the derived control scheme.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.306-319
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• 2008

Reaching fault tolerance in technological systems requires to detect malfunctions. This paper presents a diagnostic method that is robust with respect to unknown-but-bounded uncertainties of the dynamical model and the measurements. By using models of the faultless and the faulty behaviours, a state-set observer computes polyhedral sets from which the consistency of the models with the interval measurements is determined. The diagnostic result is proven to be complete, i.e., the set of faults obtained by the diagnostic algorithm includes the actual fault. The algorithm is illustrated by an application example.

• Smart Structures and Systems
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• v.22 no.5
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• pp.561-574
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• 2018

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

• Journal of The Korean Astronomical Society
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• v.36 no.1
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• pp.21-31
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• 2003

ASTRO-F /FIS will carry out all sky survey in the wavelength from 50 to 200 ${\mu}m$. At far infrared, stars and galaxies may not be good calibration sources because the IR fluxes could be sensitive to the dust shell of stars and star formation activities of galaxies. On the other hand, asteroids could be good calibration sources at far infrared because of rather simple spectral energy distribution. Recent progresses in thermal models for asteroids enable us to calculate the far infrared flux fairly accurately. We have derived the Bond albedos and diameters for 559 asteroids based on the IRAS and ground based optical data. Using these thermal parameters and standard thermal model, we have calculated the spectral energy distributions of asteroids from 10 to 200 ${\mu}m$. We have found that more than $70\%$ of our sample asteroids have flux errors less than $10\%$ within the context of the best fitting thermal models. In order to assess flux uncertainties due to model parameters, we have computed SEDs by varing external parameters such as emissivity, beaming parameter and phase integral. We have found that about 100 asteroids can be modeled to be better than $5.8\%$ of flux uncertainties. The systematic effects due to uncertainties in phase integral are not so important.

• Nuclear Engineering and Technology
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• v.48 no.1
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• pp.72-86
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• 2016

A steam explosion may occur during a severe accident, when the molten core comes into contact with water. The pressurized water reactor and boiling water reactor ex-vessel steam explosion study, which was carried out with the multicomponent three-dimensional Eulerian fuel-coolant interaction code under the conditions of the Organisation for Economic Co-operation and Development (OECD) Steam Explosion Resolution for Nuclear Applications project reactor exercise, is presented and discussed. In reactor calculations, the largest uncertainties in the prediction of the steam explosion strength are expected to be caused by the large uncertainties related to the jet breakup. To obtain some insight into these uncertainties, premixing simulations were performed with both available jet breakup models, i.e., the global and the local models. The simulations revealed that weaker explosions are predicted by the local model, compared to the global model, due to the predicted smaller melt droplet size, resulting in increased melt solidification and increased void buildup, both reducing the explosion strength. Despite the lower active melt mass predicted for the pressurized water reactor case, pressure loads at the cavity walls are typically higher than that for the boiling water reactor case. This is because of the significantly larger boiling water reactor cavity, where the explosion pressure wave originating from the premixture in the center of the cavity has already been significantly weakened on reaching the distant cavity wall.