• Journal of IKEEE
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• v.26 no.4
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• pp.653-658
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• 2022

This paper presents a Maximum Torque per Ampere (MTPA) control algorithm for an interior permanent magnet synchronous motor (IPMSM) drive considering the permanent magnet (PM) flux linkage variations due to PM temperature variation. PM flux linkage are estimated in real time via a Gopinath style stator flux linkage observer and a torque error correction factor is calculated from the estimated PM flux linkage. A 2-dimensional (2D) MTPA look-up table (LUT) is developed to achieve the MTPA trajectory reflecting PM flux linkage variation for compensating torque error occurred by parameter variation. The proposed IPMSM control algorithm is verified through simulations.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.11a
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• pp.313-318
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive fuzzy neural network controller and artificial neural network(ANN). This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using Adaptive-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper reposes speed control of IPMSM using Adaptive-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is a lied to IPMSM drive system controlled Adaptive-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the Adaptive-FNN and ANN controller.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.1
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• pp.29-37
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• 2006

Precise control of interior permanent magnet synchronous motor(IPMSM) over wide speed range is an engineering challenge. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using learning mechanism-fuzzy neural network(LM-FNN) and ANN(artificial neural network) control. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility md numerical processing capability. Also, this paper proposes speed control of IPMSM using LM-FNN and estimation of speed using artificial neural network controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. 'The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. Analysis results to verify the effectiveness of the new hybrid intelligent control proposed in this paper.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.5
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• pp.32-43
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. This paper proposes speed control of IPMSM using adaptive learning fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive learning fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive learning fuzzy neural network and artificial neural network.

• Bulletin of the Korean Space Science Society
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• 2005.04a
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• pp.129-132
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• 2005

This paper describes BAUSAT-2 orbital thermal analysis and preliminary design of thermal control subsystem. To design thermal control subsystem of HAUSAT-2, we have considered active & passive thermal control method based on basic theory and themal equilibrium equation. Using this result, suitable thermal control method and material have been selected. We have designed thermal control subsystem based on analysis of HAUSAT-2's thermal environments on sun synchronous orbit with altitude 650km, inclination $98^{\circ}$ and thermal distribution and range expectation of each HAUSAT-2's surface. Thermal analysis consists of system level, box level and board level analysis. We have completed system level and box level analysis. Till now, board level analysis of main heat dissipation board in progress. Thermal control subsystem has designed according to thermal analysis result. This design is to maintain all of the HAUSAT-2 components within the allowable temperature limits. In future, STM

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.1-6
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• 2019

In this paper, we have developed an app that can control IPTV - Miracast and the Internet with Android 7.0 on the embedded board. The app can be controlled by the remote control of IPTV and can receive TV when using IPTV. And Miracast and Embedded board are put into the power saving mode. When one mode is selected in this way, the other two modes enter the power saving mode and the power saving control technology is applied to reduce energy consumption. The board used in this paper was Android version 7.1.2 version of Raspberry pie 3B / B + and Asus Tinker board s, and Android Studio program was used to make the app and a synchronization control program was also developed to operate with IPTV remote control. The operation of the Android start program using the power saving power control technology and the remote control synchronization control program developed in this way is confirmed to be normal operation as a result of applying to the actual IPTV.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.480-480
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• 2000

In this paper, we proposed a fuzzy-neuro controller to control the speed of wound rotor induction motor with slip energy recovery. The speed is limited at some range of sub-synchronous speed of the rotating magnetic field. Control speed by adjusting resistance value in the rotor circuit that occurs the efficiency of power are reduced, because of the slip energy is lost when it passes through the rotor resistance. The control system is designed to maintain efficiency of motor. Recently, the emergence of artificial neural networks has made it conductive to integrate fuzzy controllers and neural models for the development of fuzzy control systems, Fuzzy-neuro controller has been designed by integrating two neural network models with a basic fuzzy logic controller. Using the back propagation algorithm, the first neural network is trained as a plant emulator and the second neural network is used as a compensator for the basic fuzzy controller to improve its performance on-line. The function of the neural network plant emulator is to provide the correct error signal at the output of the neural fuzzy compensator without the need for any mathematical modeling of the plant. The difficulty of fine-tuning the scale factors and formulating the correct control rules in a basic fuzzy controller may be reduced using the proposed scheme. The scheme is applied to the control speed of a wound rotor induction motor process. The control system is designed to maintain efficiency of motor and compensate power factor of system. That is: the proposed controller gives the controlled system by keeping the speed constant and the good transient response without overshoot can be obtained.

• Proceedings of the KIEE Conference
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• 2009.04b
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• pp.29-31
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• 2009

Optimal efficiency control of synchronous reluctance motor(SynRM) is very important in the sense of energy saving and conservation of natural environment because the efficiency of the SynRM is generally lower than that of other types of AC motors. This paper is proposed an efficiency optimization control for the SynRM which minimizes the copper and iron losses. The design of the speed controller based on fuzzy-neural networks (FN)-PI controller that is implemented using fuzzy control and neural networks. There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization control is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. It is shown that the current components which directly govern the torque production have been very well regulated by the efficiency optimization control scheme. The proposed algorithm allows the electromagnetic losses In variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of the proposed controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.4
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• pp.501-517
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• 2016

Synchronized switch damping (SSD) is a structural vibration control technique in which a piezoelectric patch attached to or embedded into the structure is connected to or disconnected from the shunt circuit in order to dissipate the vibration energy of the host structure. The switching process is performed by a digital signal processor (DSP) which detects the displacement extrema and generates a command to operate the switch in synchronous with the structure motion. Recently, autonomous SSD techniques have emerged in which the work of DSP is taken up by a low pass filter, thus making the whole system autonomous or self-powered. The control performance of the previous autonomous SSD techniques heavily relied on the electrical quality factor of the shunt circuit which limited their damping performance. Thus in order to reduce the influence of the electrical quality factor on the damping performance, a new autonomous SSD technique is proposed in this paper in which a negative capacitor is used along with the inductor in the shunt circuit. Only a negative capacitor could also be used instead of inductor but it caused saturation of negative capacitor in the absence of an inductor due to high current generated during the switching process. The presence of inductor in the shunt circuit of negative capacitor limits the amount of current supplied by the negative capacitance, thus improving the damping performance. In order to judge the control performance of proposed autonomous SSDNCI, a comparison is made between the autonomous SSDI, autonomous SSDNC and autonomous SSDNCI techniques for the control of an aluminum cantilever beam subjected to both single mode and multimode excitation. A value of negative capacitance slightly greater than the piezoelectric patch capacitance gave the optimum damping results. Experiment results confirmed the effectiveness of the proposed autonomous SSDNCI technique as compared to the previous techniques. Some limitations and drawbacks of the proposed technique are also discussed.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1363-1369
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• 2015

The frequency of a power system should be maintained within the allowed limits for stable operation. When a disturbance such as generator tripping occurs in a power system, the frequency is recovered to the nominal value through the inertial, primary, and secondary responses of the operating synchronous generators (SGs). However, for a power system with high wind penetration, the system inertia will decrease significantly because wind generators (WGs) are operating decoupled from the power system. This paper proposes a dynamic droop-based inertial control for a WG. The proposed inertial control determines the dynamic droop depending on the rate of change of frequency (ROCOF). At the initial period of a disturbance, where the ROCOF is large, the droop is set to be small to release a large amount of the kinetic energy (KE) and thus the frequency nadir can be increased significantly. However, as times goes on, the ROCOF will decrease and thus the droop is set to be large to prevent over-deceleration of the rotor speed of a WG. The performance of the proposed inertial control was investigated in a model system, which includes a 200 MW wind power plant (WPP) and five SGs using an EMTP-RV simulator. The test results indicate that the proposed scheme improves the frequency nadir significantly by releasing a large amount of the KE during the initial period of a disturbance.