• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.436-443
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• 2007

In this paper, an intelligent method called BELBIC (Brain Emotional Learning Based Intelligent Controller) is used to control of Locally Linear Neuro-Fuzzy Model (LOLIMOT) of Washing Machine. The Locally Linear Neuro-Fuzzy Model of Washing Machine is obtained based on previously extracted data. One of the important issues in using BELBIC is its parameters setting. On the other hand, the controller design for Washing Machine is a multi objective problem. Indeed, the two objectives, energy consumption and effectiveness of washing process, are main issues in this problem, and these two objectives are in contrast. Due to these challenges, a Multi Objective Genetic Algorithm is used for tuning the BELBIC parameters. The algorithm provides a set of non-dominated set points rather than a single point, so the designer has the advantage of selecting the desired set point. With considering the proper parameters after using additional assumptions, the simulation results show that this controller with optimal parameters has very good performance and considerable saving in energy consumption.

• Journal of Powder Materials
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• v.15 no.5
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• pp.399-404
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• 2008

Spray pyrolysis was applied to prepare $M_{3}MgSi_{2}O_{8}:Eu^{2+}$ (M=Ca, Sr, Ba) blue phosphor powder. The library of a Ca-Sr-Ba ternary system was obtained by a combinatorial method combined with the spray pyrolysis in order to optimize the luminescent property under vacuum ultraviolet (VUV) excitation. 10 potential compositions were chosen from the first screening. The emission shifted to longer wavelength as Ca became a dominant element and the emission intensity was greatly reduced in the composition region at which Ba is dominant element. On the base of the first screening result, the second fine tuning was carried out in order to optimize the luminescence intensity under VUV excitation. The optimal composition for the highest luminescence intensity was $(Ca_{1.7},\;Sr_{0.3},\;Ba_{1.0})Si_{2}O_{8}:Eu^{2+}$ which had the color coordinate of (0.152, 0.072) and about 64% emission intensity of $BaMgAl_{10}O_{17}$ (BAM) phosphor.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.408-421
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• 2015

Because the wheel of V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor (PMSM) has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming job. In order to overcome difficulties for design of the linear controllers, a hybrid recurrent Chebyshev neural network (NN) control system is proposed to control for a PMSM servo-driven V-belt CVT system under the occurrence of the lumped nonlinear load disturbances. The hybrid recurrent Chebyshev NN control system consists of an inspector control, a recurrent Chebyshev NN control with adaptive law and a recouped control. Moreover, the online parameters tuning methodology of adaptive law in the recurrent Chebyshev NN can be derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, the optimal learning rate of the parameters based on discrete-type Lyapunov function is derived to achieve fast convergence. The recurrent Chebyshev NN with fast convergence has the online learning ability to respond to the system's nonlinear and time-varying behaviors. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.

• Journal of the Korean Society of Propulsion Engineers
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• v.16 no.5
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• pp.29-36
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• 2012

3-D linear acoustic analysis has been performed to elucidate damping characteristics of large Helmholtz acoustic cavities in FASTRAC thrust chamber. Acoustic impedance concept has been introduced to quantify combustion stabilization capacity. For a given acoustic cavity, sonic velocity in the cavity to achieve an optimal tuning has been determined and satisfactory agreement with the previous results has been obtained. Feasible estimation of sonic velocity in the acoustic cavity has been devised. Results show similar trends without significant deviations, which can be used in the procedure of design and verification of acoustic cavity. From the satisfactory results, investigation of other thrust chambers with acoustic cavities which have shown combustion instabilities will be done as future works.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2606-2611
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• 2003

Comprehensive study on the control system design for a RTP process has been conducted. The purpose of the control system is to maintain maximum temperature uniformity across the silicon wafer achieving precise tracking for various reference trajectories. The study has been carried out in two stages: thermal balance modeling on the basis of a semi-empirical radiation model, and optimal iterative learning controller design on the basis of a linear state space model. First, we found through steady state radiation modeling that the fourth power of wafer temperatures, lamp powers, and the fourth power of chamber wall temperature are related by an emissivity-independent linear equation. Next, for control of the MIMO system, a state space modeland LQG-based two-stage batch control technique was derived and employed to reduce the heavy computational demand in the original two-stage batch control technique. By accommodating the first result, a linear state space model for the controller design was identified between the lamp powers and the fourth power of wafer temperatures as inputs and outputs, respectively. The control system was applied to an experimental RTP equipment. As a consequence, great uniformity improvement could be attained over the entire time horizon compared to the original multi-loop PID control. In addition, controller implementation was standardized and facilitated by completely eliminating the tedious and lengthy control tuning trial.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.546-551
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• 2004

In this paper, a level controller is designed with the neuro-fuzzy model based on Takagi-Sugeno fuzzy system. The fuzzy system is employed as the controller, which can be tuned by the neural network mechanism based on a gradient descent technique. The tuning mechanism will provide an optimal process input by forcing the process error to zero. The proposed controller provides the online tunable mode to adjust the consequent membership function parameters. The controller is implemented with M-file and graphic user interface (GUI) of Matlab program. The program uses MPIBM3 interface card to connect with the industrial processes In the experimentation, the proposed method is tested to vary of the process parameters, set points and load disturbance. Processes of one tank and two tanks are used to evaluate the efficiency of our controller. The results of the both processes are compared with two PID systems that are 3G25A-PIDO1-E and E5AK of OMRON. From the comparison results, our controller performance can be archived in the case of more robustness than the two PID systems.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.944-949
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• 2017

Recently, the demand of maglev systems in the manufacturing industry for LCD and OLED display panels, which are required to be very clean and possess vacuum systems, has been increasing due to their characteristics such as being non-contact, noise free and eco-friendly. However, it is still a challenge to simultaneously control both the propulsion and levitation for their interactive effect difficult to be exactly measured. In this paper, we proposed a new tuning method for controlling the magnetic levitation force robustly against the levitation disturbance caused by a propulsion system, based on LQ servo optimal control. The disturbance torque of the LSM propulsion system is calculated through FEM analysis in such a way that the LQ servo controller is determined in order to minimize the effect of the disturbance. The robust performance of the proposed LQ servo control method for the in-track type magnetic levitation systems is demonstrated via simulations and experiments.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• v.y2005m4
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• pp.291-298
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• 2005

Linear acoustic analysis has been performed to elucidate damping characteristics of acoustic cavities. Results have shown that resonant frequencies of acoustic cavity obtained by classical theoretic approach and present linear analysis are somewhat different from each other. This difference is due to the limitation of classical theory. To quantify the damping characteristics, acoustic impedance has been introduced and resultant absorption and conductance have been evaluated. Satisfactory agreement has been achieved with previous experiment. Finally the design procedure for optimal tuning of acoustic cavity has been established

• Journal of Power System Engineering
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• v.13 no.6
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• pp.43-50
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• 2009

If an internal combustion engine is operated by consolidated control, the minimum fuel consumption is achieved satisfying the demanded objectives. For this, it is necessary that the engine is operated on the ideal operating line which satisfies minimum fuel consumption. In this context of view, there are many tries to achieve given object. However, the parameter in the internal combustion engines are variable and depend on the operating points. Therefore, it is necessary to cope with the uncertainties such that the optimal operating may be possible. From this point of view, this paper gives a controller design method and a robust stability condition for engine torque control which satisfies the given control performance and robust stability in the presence of physical parameter perturbation. Exactly, the present paper considers a robust stability of this 2DOF servosystem with nonlinear type uncertainty in the engine system, and a robust stability condition for the servosystem is introduced. This result guarantees that if the plant uncertainty is in the permissible set defined by the given condition then a gain tuning can be carried out to suppress the influence of the plant uncertainties.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10a
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• pp.124-140
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• 1995

The merit of fuzzy rule based systems stems from their capability of encoding qualitative knowledge of experts into quantitative rules. Recent advancement in automatic tuning or self-organization of fuzzy rules from experimental data further enhances their power, allowing the integration of the top-down encoding of knowledge with the bottom-up learning of rules. In this paper, methods of self-organizing fuzzy rules and of performing defuzzification and inference is presented based on a multi-resolution radial basis function network. The network learns an arbitrary input-output mapping from sample distribution as the union of hyper-ellipsoidal clusters of various locations, sizes and shapes. The hyper-ellipsoidal clusters, representing fuzzy rules, are self-organized based of global competition in such a way as to ensute uniform mapping errors. The cooperative interpolation among the multiple clusters associated with a mapping allows the network to perform a bidirectional many-to-many mapping, representing a particular from of defuzzification. Finally, an inference engine is constructed for the network to search for an optimal chain of rules or situation transitions under the constraint of transition feasibilities imposed by the learned mapping. Applications of the proposed network to skill acquisition are shown.