• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1439-1450
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• 2013

This paper investigates a robust neuro-fuzzy control (NFC) method which can accurately follow the speed reference of an interior permanent magnet synchronous motor (IPMSM) in the existence of nonlinearities and system uncertainties. A neuro-fuzzy control term is proposed to estimate these nonlinear and uncertain factors, therefore, this difficulty is completely solved. To make the global stability analysis simple and systematic, the time derivative of the quadratic Lyapunov function is selected as the cost function to be minimized. Moreover, the design procedure of the online self-tuning algorithm is comparatively simplified to reduce a computational burden of the NFC. Next, a rotor angular acceleration is obtained through the disturbance observer. The proposed observer-based NFC strategy can achieve better control performance (i.e., less steady-state error, less sensitivity) than the feedback linearization control method even when there exist some uncertainties in the electrical and mechanical parameters. Finally, the validity of the proposed neuro-fuzzy speed controller is confirmed through simulation and experimental studies on a prototype IPMSM drive system with a TMS320F28335 DSP.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2142-2153
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• 2014

Linearization of transducer characteristic plays a vital role in electronic instrumentation because all transducers have outputs nonlinearly related to the physical variables they sense. If the transducer output is nonlinear, it will produce a whole assortment of problems. Transducers rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. Attempts have been made by many researchers to increase the range of linearity of transducers. This paper presents a method to compensate nonlinearity of Linear Variable Displacement Transducer (LVDT) based on Extreme Learning Machine (ELM) method, Differential Evolution (DE) algorithm and Artificial Neural Network (ANN) trained by Genetic Algorithm (GA). Because of the mechanism structure, LVDT often exhibit inherent nonlinear input-output characteristics. The best approximation capability of optimized ANN technique is beneficial to this. The use of this proposed method is demonstrated through computer simulation with the experimental data of two different LVDTs. The results reveal that the proposed method compensated the presence of nonlinearity in the displacement transducer with very low training time, lowest Mean Square Error (MSE) value and better linearity. This research work involves less computational complexity and it behaves a good performance for nonlinearity compensation for LVDT and has good application prospect.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.452-461
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• 2007

Adaptive anti-sway and trajectory tracking control of overhead crane is presented, which utilizes Fuzzy Uncertainty Observer(FUO) and Fuzzy based Variable Structure Control(FVSC). We consider an overhead crane system which can be decoupled into the actuated and unactuated subsystems with its own lumped uncertainty such as parameter uncertainties and external disturbance. First, a new method for anti-sway control using FVSC is proposed to improve the conventional method based on Lyapunov direct method, while a conventional trajectory tracking control law using feedback linearization is directly adopted. Second, FUO is designed to estimate one of the two lumped uncertainties which can compensate both of them, based on the fact that two lumped uncertainties are coupled with each other. Then, an adaptive anti-sway control is proposed by incorporating the proposed FVSC and FUO. Under the condition that the observation error is Uniformly Ultimately Bounded(UUB) within an arbitrarily shrinkable region, the overall closed-loop system is shown to be Globally Uniformly Ultimately Bounded(GUUB). In addition, the Global Asymptotic Stability(GAS) of it is shown under the vanishing disturbance assumption. Finally, the effectiveness of the proposed scheme has been confirmed by numerical simulations.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.6 s.312
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• pp.27-32
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• 2006

A temperature-compensated hygrometer has been developed using resistive humidity sensors. It consist of a sine wave generator, logarithm converters, rectifiers, and amplifiers. The hygrometer accomplishes the linearization and temperature compensation of sensor characteristics. The theory of operation is presented and experimental results are used to verify theoretical predictions. The experimental results show that the conversion sensitivity of the hygrometer is about 24.8 mV/%RH and the linearity error of the conversion characteristic is less than 17.2 % over a relative humidity range from 30 to 80 %RH. The results also show that the temperature coefficient of the output voltage is less than $10149ppm/^{\circ}C$ over a temperature range from 22 to $40^{\circ}C$.

• Journal of Advanced Navigation Technology
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• v.16 no.5
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• pp.738-743
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• 2012

Kalman filter is generally used in INS/GPS integrated navigation filter. However, the INS with low performance inertia sensor can not find accurate azimuth in initial alignment stage because sensor noise level is too large compare to Earth rotation rate, therefore the performance and stability of Kalman filter can not be guaranteed. In this paper, the extended Kalman filter and particle filter combined filter structure which can be overcome large initial azimuth error is proposed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.303-306
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• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKF uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.3
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• pp.50-57
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• 2003

In the paper, the design and evaluation of a helicopter trajectory tracking controller are presented. The control algorithm is implemented using the feedback linearization technique and the two time-scale separation architecture. In addition, and on-line adaptive architecture that employs a neural network compensating the model inversion error caused by the deficiency of full knowledge of helicopter dynamic is applied to augment the attitude control system. Trajectory tracking performance of the control system in evaluated using modified TMAN simulation program representing as Apache helicopter. It is show that the on-line neural network in an adaptive control architecture is very effective in dealing with the performance depreciation problem of the trajectory tracking control caused by insufficient information of dynamics.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.3
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• pp.201-210
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• 2020

2 (Langmuir, Freundlich, Elovich, Temkin, and Dubinin-Radushkevich) and 3 (Sips and Redlich-Peterson)-parameter isotherm models were applied to evaluated for the applicability of adsorption of Cu(II) and/or phosphate isotherm using chitosan bead. Non-linear and linear isotherm adsorption were also compared on each parameter with coefficient of determination (R²). Among 2-parameter isotherms, non-linear Langmuir and Freundlich isotherm showed relatively higher R² and appropriate maximum uptake (q_m) than other isotherm equation although linear Dubinin-Radushkevich obtained highest R². 3-parameter isotherm model demonstrated more reasonable and accuracy results than 2-parmeter isotherm in both non-linear and linear due to the addition of one parameter. The linearization for all of isotherm equation did not increase the applicability of adsorption models when error experiment data was included.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.403-406
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• 1998

There are two cases of error occurrence of STT(Smart Temperature Transmitter). One is that because of unstable reference voltage, data from A/D converter is not reliable. The other is that because of change of room temperature, this change affects conversion of A/D converter. In this paper, we show algorithms be adapted to STT for reliable conversion of A/D converter through a experiment and compensation for temperature change. In a experiment, we collect data from reference voltage and ground then calculate nominal value of these at constant temperature during A/D converter initialization or at any conversion time. Algorithm for compensation for unstable reference voltage calculates a correction factor and adapts it to compensation for malfunction of A/D converter. Algorithm for compensation for variation of room temperature is come from linearization of thermistor but is adapted to zener diode, not thermistor, therefor we have less effort for compensation for temperature and have a idea that it can be adapted to A/D converter system.