• Earthquakes and Structures
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• v.11 no.6
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• pp.1077-1099
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• 2016

One of the main shortcomings in the current passive base isolation system is lack of adaptability. The recent research and development of a novel adaptive seismic isolator based on magnetorheological elastomer (MRE) material has created an opportunity to add adaptability to base isolation systems for civil structures. The new MRE based base isolator is able to significantly alter its shear modulus or lateral stiffness with the applied magnetic field or electric current, which makes it a competitive candidate to develop an adaptive base isolation system. This paper aims at exploring suitable control algorithms for such adaptive base isolation system by developing a close-loop semi-active control system for a building structure equipped with MRE base isolators. The MRE base isolator is simulated by a numerical model derived from experimental characterization based on the Bouc-Wen Model, which is able to describe the force-displacement response of the device accurately. The parameters of Bouc-Wen Model such as the stiffness and the damping coefficients are described as functions of the applied current. The state-space model is built by analyzing the dynamic property of the structure embedded with MRE base isolators. A Lyapunov-based controller is designed to adaptively vary the current applied to MRE base isolator to suppress the quake-induced vibrations. The proposed control method is applied to a widely used benchmark base-isolated structure by numerical simulation. The performance of the adaptive base isolation system was evaluated through comparison with optimal passive base isolation system and a passive base isolation system with optimized base shear. It is concluded that the adaptive base isolation system with proposed Lyapunov-based semi-active control surpasses the performance of other two passive systems in protecting the civil structures under seismic events.

• Journal of Power Electronics
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• v.2 no.1
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• pp.1-10
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• 2002

An advanced active power filter for the compensation of instantaneous harmonic current components in nonlinear current load is presented in this paper. A novel signal processing technique using an adaptive neural network algorithm is applied for the detection of harmonic components generated by three-phase nonlinear current loads and this method can efficiently determine the instantaneous harmonic components in real time. The control strategy of the switching signals to compensate current harmonics of the three-phase inverter is also discussed and its switching signals are generated with the space voltage vector modulation scheme. The validity of this active filtering processing system to compensate current harmonics is substantiated on the basis of simulation results.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.778_779
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• 2009

The maximum output torque developed by the machine is dependent on the allowable current rating and maximum voltage that the inverter can supply to the machine. Therefore, to use the inverter capacity fully, it is desirable to use the control scheme considering the voltage and current limit condition, which can yield the maximum torque per ampere over the entire speed range. The paper is proposed maximum torque control of induction motor drive using adaptive learning neuro fuzzy controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d, q axis current $_i_{ds}$, $i_{qs}$ for maximum torque operation is derived. The proposed control algorithm is applied to induction motor drive system controlled adaptive learning neuro fuzzy controller and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the adaptive learning neuro fuzzy controller and ANN controller.

• Journal of Power Electronics
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• v.4 no.2
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• pp.102-111
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• 2004

This paper is concerned with a deadbeat current control implementation of shunt-type three-phase active power filter (APF). Although the one-dimensional deadbeat control method can attain time-optimal response of APF compensating current, one sampling period is actually required fur its settling time. This delay is a serious drawback for this control technique. To cancel such a delay and one more delay caused by DSP execution time, the desired APF compensating current has to be predicted two sampling periods ahead. Therefore an adaptive predictor is adopted for the purpose of both predicting the control error of two sampling periods ahead and bringing the robustness to the deadbeat current control system. By adding the adaptive predictor output as an adjustment term to the reference value of half a source voltage period before, settling time is made short in a transient state. On the other hand, in a steady state, THD (total harmonic distortion) of the utility grid side AC source current can be reduced as much as possible, compared to the case that ideal identification of controlled system could be made.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.10
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• pp.73-81
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• 2007

To improve the capability of instantaneous torque control, a design method of an MRAC-based adaptive current controller for a PMSM servo motor is proposed. In the synchronous frame current controller, a new control inputs can be obtained through the decoupling compensation. Using this, a desired controller bandwidth can be assigned However, the control performance may be degraded due to disturbances caused by the parameter variations or dead time of the switch. To improve these drawbacks, an adaptive current controller is proposed and the design method is obtained using the hyperstability theory. The asymptotic stability is proved and the effectiveness is verified through simulations and experiments using DSP TMS320C31.

• Journal of Advanced Navigation Technology
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• v.10 no.3
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• pp.191-197
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• 2006

In the paper propose a sensorless sliding mode control method of an induction motor using an adaptive speed control. The control objective is apply to adaptive speed observer instead of a encoder and to remove errors using the sliding mode current controller by parameters variation and disturbances that include the current controller. A stability of the sliding mode current controller and the adaptive speed observer using a design controller is guaranteed by the Lyapunov stability criterion. The performance of the proposed control system is demonstrated by simulation using the matlab silmulink and experimental results using induction motor show that the proposed method can apply an induction motor control.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.941-946
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• 2005

In this paper an active random noise control using adaptive learning rate neural networks is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. It is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.171-174
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• 1991

Among the various PWM methods, the hysteresis-band current control PWM method is popularly used because of its simplicity of implementation, fast response characteristics and inherent peak current limiting capability. However, the current control PWM method with a fixed hysteresis-band has the disadvantage that switching frequency decreases and current ripple is high as the increasing of back-EMF. As a result, load current contains excess harmonics. This paper describes a adaptive hysteresis-bandwidth control algorithm so as to maintain the average switching frequency constant and decrease the current ripple where the hysteresis bandwidth is derived as a relation with the switching frequency. This control algorithm is applied to the surface-type brushless DC motor with separated winding and using the computer simulation, the validity of its algorithm is proved.

• Journal of Power Electronics
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• v.12 no.3
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• pp.468-476
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• 2012

The interior permanent magnet synchronous motor (IPMSM) has been widely used in electric vehicle applications due to its excellent power to weigh ratio. This paper proposes the maximum torque control of an IPMSM drive using an adaptive learning (AL) fuzzy neural network (FNN) and an artificial neural network (ANN). This control method is applicable over the entire speed range while taking into consideration the limits of the inverter's rated current and voltage. This maximum torque control is an executed control through an optimal d-axis current that is calculated according to the operating conditions. This paper proposes a novel technique for the high performance speed control of an IPMSM using AL-FNN and ANN. The AL-FNN is a control algorithm that is a combination of adaptive control and a FNN. This control algorithm has a powerful numerical processing capability and a high adaptability. In addition, this paper proposes the speed control of an IPMSM using an AL-FNN, the estimation of speed using an ANN and a maximum torque control using the optimal d-axis current according to the operating conditions. The proposed control algorithm is applied to an IPMSM drive system. This paper demonstrates the validity of the proposed algorithms through result analysis based on experiments under various operating conditions.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.669-681
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• 2006

In this work, a new method of design adaptive controllers for SISO systems based on multiple models and switching is presented. The controller selects the model from a given set, according to a switching rule based on output prediction errors. The goal is to design, at each sample instant, a predictive control law that ensures the robust stability of the closed-loop system and achieves the best performance for the current operating point. At each sample the proposed control scheme identifies a set of linear models that best characterizes the dynamics of the current operating region. Then, it carries out an automatic reconfiguration of the controller to achieve the best possible performance whilst providing a guarantee of robust closed-loop stability. The results are illustrated by simulations a nonlinear continuous and stirred tank reactor.