• Proceedings of the KIEE Conference
• /
• 2003.07d
• /
• pp.2289-2291
• /
• 2003

NLPCA(Nonlinear Principal Component Analysis is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA can be implemented by a feedforward neural network called AANN (AutoAssociative Neural Network) which performs the identity mapping. In this work, a sensor fault detection system based on NLPCA and Maximum Likelihood Estimation scheme is presented. To verify its applicability, simulation study on the data supplied from Saemangeum measurement stations is executed.

• Proceedings of the KIEE Conference
• /
• 1994.11a
• /
• pp.159-161
• /
• 1994

A new control technique which generates high-quality sinusoidal output voltage from a single-phase resonant do link inverter suitable for the UPS systems is presented. The inverter output voltage control system has the PID controller with a minor loop of the filter inductor current and tile feedforward controller. The proposed control scheme also solves resonant voltage overshoot without any additional switch or passive component, resulting in pulses with uniform amplitude and high efficiency. Experimental results in the case of linear and nonlinear loads are presented to confirm the usefulness of the Proposed control algorithms.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.2283-2285
• /
• 2001

This paper proposed to a neural network based fuzzy control (neuro-fuzzy control) technique for attitude control of helicopter with strongly dynamic nonlinearities and derived a helicopter aerodynamic torque equation of helicopter and the force balance equation. A neuro-fuzzy system is a feedforward network that employs a back-propagation algorithm for learning purpose. A neuro-fuzzy system is used to identify nonlinear dynamic systems. Hence, this paper presents methods for the design of a neural network(NN) based fuzzy controller(that is, neuro-fuzzy control) for a helicopter of nonlinear MIMO systems. The proposed neuro-fuzzy control determined to a input-output membership function in fuzzy control and neural networks constructed to improve through learning of input-output membership functions determined in fuzzy control.

• Journal of the Korean Society for Precision Engineering
• /
• v.21 no.5
• /
• pp.55-63
• /
• 2004

This paper presents neural network based controller using the feedback error loaming technique for a heavy load pointing system. Also the mathematical model was developed to analyze heavy load pointing system. The control scheme consists of a feedforward neural network controller and a fixed-gain feedback controller. This neural network controller is trained so as to make the output of the feedback controller zero. The proposed controller is compared with the conventional PI controller through simulations, and the results show that the pointing accuracy of the proposed control system are improved against the disturbance induced by vehicle running on the bump course.

• ETRI Journal
• /
• v.45 no.4
• /
• pp.690-703
• /
• 2023

Multistage amplifiers have become appropriate choices for high-speed electronics and data conversion. Because of the large number of high-impedance nodes, frequency compensation has become the biggest challenge in the design of multistage amplifiers. The new compensation technique in this study uses two differential stages to organize feedforward and feedback paths. Five Miller loops and a 500-pF load capacitor are driven by just two tiny compensating capacitors, each with a capacitance of less than 10 pF. The symbolic transfer function is calculated to estimate the circuit dynamics and HSPICE and TSMC 0.18 ㎛. CMOS technology is used to simulate the proposed five-stage amplifier. A straightforward iterative approach is also used to optimize the circuit parameters given a known cost function. According to simulation and mathematical results, the proposed structure has a DC gain of 190 dB, a gain bandwidth product of 15 MHz, a phase margin of 89°, and a power dissipation of 590 ㎼.

• Proceedings of the KIPE Conference
• /
• 2018.11a
• /
• pp.95-97
• /
• 2018

The power quality of Single Phase Grid-Connected Inverters (GCIs) has received much attention with the increasing number of Distributed Generation (DG) systems. However, the performance of single phase GCIs get degraded due to several factors such as the grid voltage harmonics, the dead time effect, and the turn ON/OFF of the switches, which causes the harmonics at the output of GCIs. Therefore, it is not easy to satisfy the harmonic standards such as IEEE 519 and P1547 without the help of harmonic compensator. To meet the harmonic standards a certain kind of harmonic controller needs to be added to the current control loop to effectively mitigate the low order harmonics. In this paper, the harmonic compensation is performed using a novel robust harmonic compensation method based on Digital Lock-in Amplifier (DLA). In the proposed technique, DLAs are used to extract the amplitude and phase information of the harmonics from the output current and compensate it by using a simple PI controller in the feedforward manner. In order to show the superior performance of the proposed harmonic compensation technique, it is compared with those of conventional harmonic compensation methods in terms of the effectiveness of harmonic elimination, complexity, and implementation. The validity of the proposed harmonic compensation techniques for the single phase GCIs is verified through the experimental results with a 5kW single phase GCI. Index Terms -Single Phase Grid Connected Inverter (SPGCI), Harmonic Compensation Method, Total Harmonic Distortion (THD) and Harmonic Standard.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.35 no.5
• /
• pp.267-275
• /
• 2022

Using a nonconforming mesh in enrichment methods results in several numerical issues induced by discontinuities and singularities found within the solution spaces, including the computational overhead during integration. In this study, we present a novel enrichment technique based on the selective expansion technique of moment fitting (Düster and Allix, 2020). In particular, two modifications are proposed to address the inefficiency during the integration process. First, a feedforward artificial neural network is introduced to correlate the implicit functions and integration moments. Through numerical examples, it is shown that the efficiency of the method is greatly improved when compared with existing expansion techniques, whereas the solution accuracy is maintained. Additionally, the finite element and domain representation grids are separated, which in turn improves the solution accuracy even for coarse mesh conditions.

• Journal of Institute of Control, Robotics and Systems
• /
• v.10 no.2
• /
• pp.112-124
• /
• 2004

A neural network based adaptive reconfigurable flight controller is presented for a class of discrete-time nonlinear flight systems in the presence of variations of aerodynamic coefficients and control effectiveness decrease caused by control surface damage. The proposed adaptive nonlinear controller is developed making use of the backstepping technique for the angle of attack, sideslip angle, and bank angle command following without two time separation assumption. Feedforward multilayer neural networks are implemented to guarantee reconfigurability for control surface damage as well as robustness to the aerodynamic uncertainties. The main feature of the proposed controller is that the adaptive controller is developed under the assumption that all of the nonlinear functions of the discrete-time flight system are not known accurately, whereas most previous works on flight system applications even in continuous time assume that only the nonlinear functions of fast dynamics are unknown. Neural networks learn through the recursive weight update rules that are derived from the discrete-time version of Lyapunov control theory. The boundness of the error states and neural networks weight estimation errors is also investigated by the discrete-time Lyapunov derivatives analysis. To show the effectiveness of the proposed control law, the approach is i]lustrated by applying to the nonlinear dynamic model of the high performance aircraft.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.11 no.3
• /
• pp.97-105
• /
• 1997

The aim of this paper is to introduce a design technique of the Two-Degree-of-Freedom(TDF) satellite-tracking control system which has not only the robust stability for a unstructured uncertainty but also the robust performance for a structured uncertainty. This TDF system which can design the feedforward controller KI and the feedback one K independently is designed by , $\mu$-synthesis. The effectiveness of this TDF system is verified and compared with the One-Degree-of -Freedom(ODF) satellitetracking control system by computer simulation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.25 no.4
• /
• pp.45-56
• /
• 2011

This paper is proposed artificial neural network(ANN) rotor resistance estimation of induction motor drive controlled by multi-adaptive fuzzy learning controller(AFLC). A simple double layer feedforward ANN trained by the back-propagation technique is employed in the rotor resistance identification. In this estimator, double models of the state variable estimations are used; one provides the actual induction motor output states and the other gives the ANN model output states. The total error between the desired and actual state variables is then back propagated to adjust the weights of the ANN model, so that the output of this model tracks the actual output. When the training is completed, the weights of the ANN correspond to the parameters in the actual motor. The estimation and control performance of ANN and multi-AFLC is evaluated by analysis for various operating conditions. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.