• 한국안전학회지
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• 제14권2호
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• pp.52-61
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• 1999

This paper proposes the application of fuzzy control for high performance control of induction motor using electric vehicles. A fuzzy controller converts a set of liguistic rules based on expert knowledge into a automatic control strategy. Such controllers have often been found superior to conventional controllers especially when information being processed is inexact and uncertain. A system with fast torque response is very beneficial in applications where direct self control (DSC) is highly desirable. The response of DSC is slower during startup and during change in command torque. Fuzzy control is used for implementation of DSC to improve its slow response. Simulation implementation of the fuzzy logic controller was carried out to verify the behavior of the controller. The simulation results with fuzzy control are compared with those of the conventional DSC. The starting flux and torque response and the responses to the step changes in command torque with fuzzy implementation show a considerable improvement over the conventional control. The steady state responses in both the cases are the same.

• Journal of Electrical Engineering and Technology
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• 제11권2호
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• pp.425-436
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• 2016

In this paper, a parameter optimization based iterative learning control strategy is presented for permanent magnet synchronous motor control. This paper analyzes the mechanism of iterative learning control suppressing PMSM torque ripple and discusses the impact of controller parameters on steady-state and dynamic performance of the system. Based on the analysis, an optimization problem is constructed, and the expression of the optimal controller parameter is obtained to adjust the controller parameter online. Experimental research is carried out on a 5.2kW PMSM. The results show that the parameter optimization based iterative learning control proposed in this paper achieves lower torque ripple during steady-state operation and short regulating time of dynamic response, thus satisfying the demands for both steady state and dynamic performance of the speed regulating system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1474-1477
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• 2005

This paper is proposed maximum torque per ampere of induction motor using fuzzy-neural networks controller. Operation of maximum torque per ampere is achieved when, at a given torque and speed, the slip frequency is adjusted to that so that the stator current amplitude is minimized. This paper introduces a induction motor drive system with fuzzy-neural networks controller. A neural network-based architecture is described for fuzzy logic control. The characteristic rule and their membership function of fuzzy system are represented as the processing nodes in the neural network structure. This paper is proposed the analysis as well as the simulation results to verify the effectiveness of the new method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 A
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• pp.25-27
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• 1998

The switched reluctance motor(SRM) drive system provides a good adjustable speed and torque characteristics. However, it also has some drawbacks such as relatively high torque ripple and acoustic noise which are caused by the torque production mechanism. To reduce torque ripple and to have precise speed control, PLL technique is adopted. The PLL system in conjunction with dynamic dwell angle control scheme has good speed regulation characteristics. Digital control system with a 80c196kc micro-controller is used to be realized this drive system. Test results show that the suggested control system has the ability of dynamic and precise speed control.

• Journal of Electrical Engineering and Technology
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• 제4권1호
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• pp.66-78
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• 2009

This paper introduces the design of a fuzzy logic controller in conjunction with direct torque control strategy for a Permanent Magnet synchronous machine. A stator flux angle mapping technique is proposed to reduce significantly the size of the rule base to a great extent so that the fuzzy reasoning speed increases. Also, a fuzzy resistance estimator is developed to estimate the change in the stator resistance. The change in the steady state value of stator current for a constant torque and flux reference is used to change the value of stator resistance used by the controller to match the machine resistance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.1011-1012
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• 2007

The paper is proposed maximum torque control of SynRM drive using learning mechanism-fuzzy neural network(LM-FNN) controlle. 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-axis current ${^{i}}_d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled LM-FNN 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 LM-FNN controller.

• 전력전자학회논문지
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• 제3권3호
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• pp.263-272
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• 1998

본 논문은 자기적 비선형성과 상간의 토오크중첩을 고려하여 맥동토오크를 최소화시킬 수 있는 제어기법을 제시하고 이를 DSP를 이용하여 구현한 SRM의 구동시스템에 대하여 기술하였다. 이를 위해 먼저 몇 단계의 상전류 크기에 대한 회전자 위치별 인덕턴스값과 발생토오크의 크기를 실측한 후 이들 대표값으로부터 신경회로망을 사용하여 제어에 필요한 충분히 정밀한 값들을 추론한다. 또한 각 상간의 토오크중첩을 고려하여 총합 발생토오크의 맥동성분의 최소화되도록 상별 지령토오크 파형을 설정한다. 회전자위치와 상전류를 검출하고, 이 검출값과 추론된 토오크데이터를 이용하여 구한 토오크 값이 지령토오크를 추종하도록 델타변조기법에 의한 제어기를 구성한다. 이러한 제어방식을 실시간으로 처리하고, 제어기의 신뢰성을 높이기 위해 DSP를 사용하였다.

• 제어로봇시스템학회논문지
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• 제21권12호
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• pp.1160-1166
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• 2015

This paper proposes a decentralized robust adaptive control scheme for robot manipulators with input torque saturation in the presence of uncertainties. The control system should consider the practical problems that the controller gain coefficients of each joint may be nonlinear time-varying and the input torques applied at each joint are saturated. The proposed robot controller overcomes the various uncertainties and the input saturation problem. The proposed controller is comparatively simple and has no robot model parameters. The proposed controller is adjusted by the adaptation laws and the stability of the control system is guaranteed by the Lyapunov function analysis. Simulation results show the validity and robustness of the proposed control scheme.

• 전기학회논문지P
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• 제54권1호
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• pp.20-29
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• 2005

This paper presents an implementation of efficiency optimization of reluctance synchronous motor (RSM) using a neural network (NN) with a direct torque control (DTC). The equipment circuit considered with iron losses in RSM is analyzed theoretically, and the optimal current ratio between torque current and exiting current component are derived analytically. For the RSM driver, torque dynamic can be maintained with DTC using TMS320F2812 DSP Controller even with controlling the flux level because a torque is directly proportional to the stator current unlike induction motor. In order to drive RSM at maximum efficiency and good dynamics response, the Backpropagation Neural Network is adapted. The experimental results are presented to validate the applicability of the proposed method. The developed control system show high efficiency and good dynamic response features with 1.0 [kW] RSM having 2.57 inductance ratio of d/q.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.780-785
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• 1992

We present a multi-sensor fusion method in positioning control of a robot by using fuzzy logic. In general, the vision sensor is used in the gross motion control and the force/torque sensor is used in the fine motion control. We construct a fuzzy logic controller to combine the vision sensor data and the force/torque sensor data. Also, we apply the fuzzy logic controller to the peg-in-hole process. Simulation results uphold the theoretical results.