• Journal of Power Electronics
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• v.14 no.6
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• pp.1147-1156
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• 2014

This study proposes a quasi-fixed-frequency hysteresis current tracking control strategy for modular multilevel converters (MMCs) on the basis of voltage partition principle. First, by monitoring the grid voltage and the deviation between the output and reference currents, the output voltage is determined, thus prompting the output current to quickly and efficiently track the given current. Second, the voltages of the upper/lower capacitor of the arm and the voltages between the upper and lower arms are balanced by combining these arms with virtual loop mapping and arm voltage balance control, respectively. In particular, the proposed method is designed for any level and number of sub-modules. The validity of the proposed method is verified by simulations and experimental results of a five-level MMC prototype.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.3
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• pp.442-450
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• 1994

In this paper, the tracking of SFH/MFSK signals by using a paeallel correlator and a bank of BPF which is implemented by DFT recursively is studied. During symbol period, M-ary signal`s spectrum is analyzed by the step of n multiple of sampling period. The bank of BPF output which is stored for hop duration input to the parallel correlator. The time difference of the receiver and the transmitter is corrected by using sampling position and correlation time at which the largest output of correlator is generated. Syncronization signal detection rate and distribution of the largest output of correlator are evaluated by computer simulation in HF channel evironments for the performance analysis of proposed tracking method.

• Proceedings of the KIPE Conference
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• 2007.07a
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• pp.74-76
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• 2007

In this paper a maximum power point tracking(MPPT) techniques for power of PV(photovoltaic) systems are presented using boost converter for a connected single phase inverter. On the basic principle of power generation for the PV module, algorithms for maximum power point tracking are described by utilizing a boost converter to adjust the output voltage of the PV module. Based on output power of a boost converter, single phase inverter uses predicted current control to control four IGBT's switch in full bridge. Furthermore a low cost control system for solar energy conversion using the DSP is developed, based on boost converter to adjust the output voltage of the PV module. The effectiveness of the proposed inverter system is confirmed experimentally and by means of simulation. Finally, experimental results confirm the superior performance of the proposed method.

• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1236-1249
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• 2020

In a most recent object tracking research work, applying Convolutional Neural Network and Recurrent Neural Network-based strategies become relevant for resolving the noticeable challenges in it, like, occlusion, motion, object, and camera viewpoint variations, changing several targets, lighting variations. In this paper, the LSTM Network-based Tracking association method has proposed where the technique capable of real-time multi-object tracking by creating one of the useful LSTM networks that associated with tracking, which supports the long term tracking along with solving challenges. The LSTM network is a different neural network defined in Keras as a sequence of layers, where the Sequential classes would be a container for these layers. This purposing network structure builds with the integration of tracking association on Keras neural-network library. The tracking process has been associated with the LSTM Network feature learning output and obtained outstanding real-time detection and tracking performance. In this work, the main focus was learning trackable objects locations, appearance, and motion details, then predicting the feature location of objects on boxes according to their initial position. The performance of the joint object tracking system has shown that the LSTM network is more powerful and capable of working on a real-time multi-object tracking process.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.578-581
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• 1996

A torque trajectory control of the IPM synchronous motor using an adaptive input-output linearization technique is proposed. The input-output linearization is performed using the estimated torque output with the knowledge of machine parameters. The linearized model gives the output torque error under the variation of the flux linkage. To give a good torque tracking in the presence of the flux linkage variation, the flux linkage will be estimated where the adaptation law h derived by the Popov's hyperstability theory and the positivity concept. This estimated value is also used for the generation of the d-axis current command for the maximum torque control. Thus, a good torque tracking and the exact maximum torque-per-current operation will be obtained.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.10
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• pp.47-58
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• 2011

This paper proposes the optimal current detect(OCD) maximum power point tracking(MPPT) control of photovoltaic(PV) system for robust with environment changing. The output characteristics of the solar cell is a nonlinear and affected by a temperature, the solar radiation and temperature. Conventional MPPT control methods are tracked the maximum power point by constant incremental value. So these methods are slow the response speed and generated the vibration in steady state and cannot track the MPP in environment condition changing. And power loss is generated because of the self-excitation vibration in MPP region. To solve this problem, this paper proposes the novel control algorithm. Proposed algorithm is detected the optimal current in two control region using the output power and current curve. Detected current is used the converter switching for tracking the MPP. Proposed algorithm is compared output power error to conventional algorithm with radiation and temperature changing. In addition, the validity of the algorithm is proved through the output error response characteristics.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.11
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• pp.990-998
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• 2007

Adaptive output feedback control technique using Neural Networks(NN) is proposed for uncertain nonlinear Multi-Input Multi-Output(MIMO) systems. Modified Dynamic Inversion Model(MDIM) is introduced to decouple uncertain nonlinearities from inversion-based control input. MDIM consists of approximated dynamic inversion model and inversion model error. One NN is applied to compensate the MDIM of the system. The output of the NN augments the tracking controller which is based upon a filtered error approximation with online weight adaptation laws which are derived from Lyapunov's direct method to guarantee tracking performance and ultimate boundedness. Several numerical results are illustrated in the simulation of Van der Pol system and unmanned helicopter with model uncertainties.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.2
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• pp.19-25
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• 2003

For a class of multi-input multi-output nonlinear systems which perform a given task repetitively, an extended type of a direct leaning control (DLC) is proposed using the information on the (vector) relative degree of a multi-input multi-output system. Existing DLC methods are observed to be applied to a limited class of systems with the relative degree one and a new DLC law is suggested which can be applied to systems having higher relative degree. Using the proposed control law, the control input corresponding to the new desired output trajectory is synthesized directly based on the control inputs obtained from the learning process for other output trajectories. To show the validity and the performance of the proposed DLC, simulations are performed for trajectory tracking control of a two-axis SCARA robot.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.8
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• pp.689-696
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• 2000

In this paper we propose an eminent controller for wheeled mobile robots. This controller consists of an input-output linearization controller trying to stabilize the system and a neural network controller to compensate for uncertainties. The uncertainties are divided into two parts. First unstructured uncertainties include the elements related with system order such as friction disturbance. Second structure uncertainties are the incorrect system parameters A neural network structure of the proposed overall controller learns structural errors of the wheeled mobile robots with uncertainties and includes the neural network output. This controller learns quickly the model and has good tracking performance Simulation results show that the proposed controller is more efficient than analog controllers.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.223-233
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• 2008

A new output feedback controller design approach for flexible-joint (FJ) robots via the observer dynamic surface design technique is presented. The proposed approach only requires the feedback of position states. We first design an observer to estimate the link and actuator velocity information. Then, the link position tracking controller using the observer dynamic surface design procedure is developed. Therefore, the proposed controller can be simpler than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop system are uniformly ultimately bounded. Finally, the simulation results of a three-link FJ robot are presented to validate the good position tracking performance of the proposed control system.