• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.48-53
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• 1989

We attempt to achieve both high dynamic performance and maximal power efficiency by means of linear decoupling of rotor speed (or motor torque) and rotor flux. The induction motor with our controller possesses the input-output dynamic characteristics of a linear system such that the rotor speed (or motor torque) and the rotor flux are decoupled. The rotor speed (or motor torque) responses are not affected by abrupt changes in the rotor flux and vice versa. The rotor flux need not be measured but is estimated by the well-known flux simulator. The effect of large variation in the rotor resistance on the control performances is minimized by employing a parameter adaptation method. To illuminate the significance of our work. we present simulation and experimental results as well as mathematical performance analyses.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.5
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• pp.413-418
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• 2002

This paper proposed a feed-forward adaptive position controller that is robust for variable Inertia. The control system consists of PI Position controller, feed-forward and model reference adaptive control. A parameter g(t) of the feed-forward adaptive position controller is adapted by using both the reference model speed and position error. So it improves the transient response and reduces the settling time. And normalization function Is used to make linear adaptation time. The validity of the feed-forward adaptive controller is confirmed by simulation results.

• Journal of Communications and Networks
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• v.14 no.6
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• pp.619-628
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• 2012

The ZigBee network has been considered to monitor electricity usage of home appliances in the smart grid network. ZigBee, however, may suffer from a coexistence problem with wireless local area network (WLAN). In this paper, to resolve the coexistence problem between ZigBee network and WLAN, we propose a new protocol constructing a cognitive smart grid network for supporting monitoring of home appliances. In the proposed protocol, home appliances first estimates the transmission timing and channel information of WLAN by reading request to send/clear to send (RTS/CTS) frames of WLAN. Next, based on the estimated information, home appliances transmit a data at the same time as WLAN transmission. To manage the interference between WLAN and smart grid network, we propose a cognitive beamforming algorithm. The beamforming algorithm is designed to guaranteeing zero interference to WLAN while satisfying a required rate for smart metering. We also propose an energy efficient rate adaptation algorithm. By slowing down the transmission rate while satisfying an imperceptible impact of quality of service (QoS) of the receiver, the home appliance can significantly save transmit power. Numerical results show that the proposed multiple antenna technique provides reliable communications for smart metering with reduced power comparing to the simple transmission technique.

• ETRI Journal
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• v.32 no.1
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• pp.33-43
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• 2010

The increasing demand for non-sinusoidal currents affects the quality of distribution networks. Harmonic detection is a crucial step in the cancellation of those components by active power filters. In this paper, the discrete cosine transform (DCT) is compared with different implementations based on Fourier transforms, demonstrating their equivalences and the advantages provided by the former. We demonstrate that the phase error in the presence of grid frequency deviations and the transient length are reduced by half in comparison to the discrete Fourier transform. A novel algorithm is developed to provide frequency adaptation to the DCT, taking advantage of its good features. The window width is adjusted in real time according to the actual value of the grid fundamental frequency by means of a phase-locked loop. A technique based on dithering is employed to overcome the limitation caused by the truncation of the window number of samples, so the frequency resolution is enhanced. The theoretical approach is verified by simulated and experimental results.

• Journal of Power Electronics
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• v.14 no.5
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• pp.1008-1027
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• 2014

An electric scooter with a V-belt continuously variable transmission (CVT) driven by a permanent magnet synchronous motor (PMSM) has a lot of nonlinear and time-varying characteristics, and accurate dynamic models are difficult to establish for linear controller designs. A PMSM servo-drive electric scooter controlled by a novel hybrid modified recurrent Legendre neural network (NN) control system is proposed to solve difficulties of linear controllers under the occurrence of nonlinear load disturbances and parameters variations. Firstly, the system structure of a V-belt CVT driven electric scooter using a PMSM servo drive is established. Secondly, the novel hybrid modified recurrent Legendre NN control system, which consists of an inspector control, a modified recurrent Legendre NN control with an adaptation law, and a recouped control with an estimation law, is proposed to improve its performance. Moreover, the on-line parameter tuning method of the modified recurrent Legendre NN is derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, two optimal learning rates for the modified recurrent Legendre NN are derived to speed up the parameter convergence. Finally, comparative studies are carried out to show the effectiveness of the proposed control scheme through experimental results.

• Journal of Power Electronics
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• v.15 no.4
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• pp.974-986
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• 2015

A novel inverter fault-tolerant control scheme is proposed to drive brushless DC motor. A fault-tolerant inverter and its three fault-tolerant schemes (i.e., phase A fault-tolerant, phase B fault-tolerant, and phase C fault-tolerant) are analyzed. Eight voltage vectors are summarized and a voltage vector selection table is used in the control scheme to improve the midpoint current of the split capacitors. A stator flux observer is proposed. The observer can improve flux estimation, which does not require any speed adaptation mechanism and is immune to speed estimation error. Global stability of the flux observer is guaranteed by the Lyapunov stability analysis. A novel stator resistance estimator is incorporated into the sensorless drive to compensate for the effects of stator resistance variation. DC offset effects are mitigated by introducing an integral component in the observer gains. Finally, a control system based on the control scheme is established. Simulation and experiment results show that the method is correct and feasible.

• Journal of Power System Engineering
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• v.21 no.4
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• pp.42-50
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• 2017

A velocity controller using a modified model reference adaptive controller (M-MRAC) and a projection operator for a fish sorting belt conveyor system with uncertainty parameters, input saturation and bounded disturbances is proposed in this paper. To improve the tracking performance and robustness of the proposed controller in the presence of bounded disturbances, the followings are done. Firstly, the reference model for the conventional model reference adaptive controller (CMRAC) is replaced by a modified reference model for a M-MRAC to reduce unexpected high frequency oscillation in control input signal when the adaptation rate is increased. Secondly, estimated parameters in an adaptive law are varied smoothly under bounded external disturbances and a projection operator is utilized in an adaptive law for the proposed M-MRAC controller to be robust. Thirdly, an auxiliary error vector is introduced for compensating the error dynamics of the system when the saturation input occurs. Finally, the experimental results are shown to verify the better effectiveness and performance of the proposed controller under the bounded disturbance and saturated input than that of a CMRAC.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3821-3841
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• 2019

Cloud radio access networks (C-RANs) have been regarded in recent times as a promising concept in future 5G technologies where all DSP processors are moved into a central base band unit (BBU) pool in the cloud, and distributed remote radio heads (RRHs) compress and forward received radio signals from mobile users to the BBUs through radio links. In such dynamic environment, automatic decision-making approaches, such as artificial intelligence based deep reinforcement learning (DRL), become imperative in designing new solutions. In this paper, we propose a generic framework of autonomous cell activation and customized physical resource allocation schemes for energy consumption and QoS optimization in wireless networks. We formulate the problem as fractional power control with bandwidth adaptation and full power control and bandwidth allocation models and set up a Q-learning model to satisfy the QoS requirements of users and to achieve low energy consumption with the minimum number of active RRHs under varying traffic demand and network densities. Extensive simulations are conducted to show the effectiveness of our proposed solution compared to existing schemes.

• Smart Structures and Systems
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• v.32 no.3
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1635-1641
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• 2021

Portable systems with built-in active noise control is required low power operation. Excessive anti noise search operation can lead to rapid battery consumption. A method that can adaptively cancel noise according to the operating conditions of the system is required and the methods of reducing power are becoming very important key feature in today's portable systems. In this paper, we propose the method of active noise control(ANC) using binary search algorithm in noisy systems. The implemented architecture detects a frequency component considered as noise from the input signal and by using the binary search algorithm, the system find out an appropriate amplitude value for anti-noise in a much faster time than the general linear search algorithm. Through the experimental results, it was confirmed that the proposed algorithm performs a successful functional operation.