• 한국지능시스템학회논문지
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• 제18권5호
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• pp.642-648
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• 2008

여자유도 매니퓰레이터의 동적 제어는 관절에 가해지는 토크를 최소화하는 목적으로 많은 연구가 이루어져 왔다. 그러나 기존의 국소 토크 최적화의 동적 제어 방법은 드라이버로 구현하기 힘든 토크가 요구된다. 본 논문에서는 그러한 큰 토크 요구를 상당히 개선시킨 새로운 제어 알고리즘을 제안한다. 이 알고리즘은 기존의 국소 토크 최소화 알고리즘에 퍼지 로직과 유전자 알고리즘을 적용시킨 것이다. 제안된 알고리즘은 3자유도 평면 여자유도 로봇에 적용하였으며, 시뮬레이션 결과를 통하여 제안된 알고리즘의 타당성을 확인하였다.

• Journal of Electrical Engineering and Technology
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• 제7권4호
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• pp.546-557
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• 2012

In Conventional Combined Vector and Direct Controls (VC-DTC) of induction motor, stator current is very rich in harmonic components. It leads to high torque ripple of induction motor in high and low speed region. To solve this problem, a control method based on the concept of fuzzy logic approach is used. The control scheme proposed uses stator current error as variable. Through the fuzzy logic controller rules, the choice of voltage space vector is optimized and then torque and speed are controlled successfully with a less ripple level in torque response, which improve the system's performance. Simulation results trough MATLAB/SIMULINK${(R)}$ software gave results that justify the claims.

• Transactions on Control, Automation and Systems Engineering
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• 제2권1호
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• pp.56-61
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• 2000

Majority of industrial robots are controlled by a simple independent joint control of joint actuators rather than complex controllers based on the nonlinear dynamic model of the robot manipulator. In this independent joint control scheme, the performance of actuator control is influenced significantly by the joint disturbance torques including gravity, Coriolis and centrifugal torques, which result in the trajectory tracking error in the joint control system. The control performance of a redundant manipulator under independent joint control can be improved by minimizing this joint disturbance torque in resolving the kinematic redundancy. A 3 DOF planar robot is studied as an example, and the dynamic programming method is used to find the globally optimal joint trajectory that minimize the joint disturbance torque over the entire motion. The resulting solution is compared with the solution obtained by the conventional joint torque minimization, and it is shown that joint disturbance can be reduced using the kinematic redundancy.

• Journal of Mechanical Science and Technology
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• 제15권7호
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• pp.831-838
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• 2001

Torque ripple control of brushless DC motor has long been the main issue of the servo drive systems in which the speed fluctuation, vibration and acoustic noise need to be minimized. The vast majority of the methods for suppressing the torque ripple require the Fourier series analysis and either the iterative or least mean square minimization. In this paper, a novel approach based on the d-q-0 reference frame that achieves ripple-free torque control with maximum efficiency is presented. The proposed method optimizes the reference phase current waveforms including even the case of 3-phase unbalanced condition, and the motor winding currents are controlled to track the optimized current waveforms by the delta modulation technique. As a results, the proposed approach provides a simple and yet effectine means for obtaining the optimal motor excitation currents. The validity and applicability of the proposed control scheme are verified through simulations and experimental investigations.

• 전기학회논문지
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• 제66권12호
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• pp.1721-1724
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• 2017

This paper presents a numerical optimization technique to reduce input current and torque ripple of the low voltage BLDC motor using core, coil and switching angle optimization. The optimization technique is employed using the generalized response surface method(RSM) and sampling minimization technique with FEM. A 50W 24V BLDC motor is used to verify the proposed algorithm. As optimizing results, the input current is reduced from 2.46 to 2.11[A], and the input power is reduced from 59 [W] to 51 [W] at the speed of 1000 [rpm]. Also, applied the same optimization algorithm, the torque ripple is reduced about 7.4 %. It is confirmed that the proposed technique is a reasonably useful tool to reduce the consuming current and torque ripple of the low voltage BLDC motor for a compact and efficient design.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 전력전자학술대회 논문집
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• pp.197-199
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• 1998

A neuro-fuzzy based torque profile model of SRM with considerably improved accuracy is obtained using the measured data for training. The inferred torque profiles, which comprise magnetic non-linearities, represent the dynamic model of SRM. Then the reference torque signal with optimized waveform and switching angle are decided to control the torque directly. Hence, the presented scheme controls the torque in an instantaneous basis, allowing powerful torque control with minimum torque ripple even during the transient operation of the motor. Simulation and experimental results demonstrating the effectiveness of the proposed torque control scheme are presented.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제53권5호
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• pp.332-337
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• 2004

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying. parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• 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.

• 제어로봇시스템학회논문지
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• 제13권5호
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• pp.429-433
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• 2007

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.207-209
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• 2006

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and ststor resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.