• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2552-2556
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• 2005

In general, the forklift driven by DC motor drive system is used in the industrial field. Classically, the DC motor is controlled by current control using proportion control method, by output torque following the load on the plane like a manual operation. But in the industrial field, the forklift is demanded the robust drive mode. Some cases of the mode, there aretrouble in torque control following slope capacity. The control is sensitive concerning about slope angle and output speed, various control method is studied for stability of speed control. In this paper, I apply current control for the self-tuning using the fuzzy controller to obtain robust, stable speed control and use stable, high efficiency control using DSP as main controller for high speed processor, embody dynamic characteristic of control compared the PI controller to the fuzzy controller.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2001년도 Proceedings ICPE 01 2001 International Conference on Power Electronics
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• pp.355-359
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• 2001

This paper presents an implementation of digital control system of speed sensorless for Reluctance Synchronous Motor (RSM) drives with DTC. The control system consists of stator flux observer, rotor position/speed/torque estimator, two hysteresis band controllers, an optimal switching look-up table, IGBT voltage source inverter, and TMS320C31 DSP controller by using fully integrated control software. The stator flux observer is based on the combined voltage and current model with stator flux feedback adaptive control that inputs are current and voltage sensing of motor terminal with estimated rotor angle for wide speed range. The rotor position is estimated by observed stator flux-linkage space vector. The estimated rotor speed is determined by differentiation of the rotor position used only in the current model part of the flux observer for a low speed operating area. It does not require the knowledge of any motor parameters, nor particular care for motor starting, In order to prove the suggested control algorithm, we have a simulation and testing at actual experimental system. The developed sensorless control system is shown a good speed control response characteristic results and high performance features in 50/1000 rpm with 1.0Kw RSM having 2.57 ratio of d/q reluctance.

• Journal of Power Electronics
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• 제2권2호
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• pp.146-153
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• 2002

this paper presents an implementation of digital control system of speed sensorless for Reluctance Synchronous Motor (RSM) drives with direct torque control (DTC). The problem of DTC for high-dynamic performance RSM drive is generating a nonlinear torque due to a saturated nonlinear inductance curve with various load currents. The control system consists of stator flux observer, compensating inductance look-up table, rotor position/speed/torque estimator, two hysteresis band controllers, an optimal switching look-up table, IGBT voltage source unverter, and TMS320C31 DSP controller. The stator flux observer is based on the combined voltage and current model with stator flux feedback adapitve control that inputs are the compensated inductances, current and voltage sensing of motor terminal with estimated rotor angle for wide speed range. The rotor position is estimated rotor speed is determined by differentiation of the rotor position used only in the current model part of the flux observer for a low speed operation area. It does not requrie the knowledge of any montor paramenters, nor particular care for moter starting, In order to prove the suggested control algorithm, we have simulation and testing at actual experimental system. The developed sensorless control system is showing a good speed control response characterisitic result and high performance features in 20/1500 rpm with 1.0Kw RSM having 2.57 ratio of d/q reluctance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.780_781
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• 2009

This paper proposes efficiency optimization control of IPMSM drive using hybrid intelligent controller(HIC). The design of the speed controller based on fuzzy-neural network that is implemented using fuzzy control and neural network. The design of the current based on adaptive fuzzy control using model reference and the estimation of the speed based on neural network using ANN controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The optimal current can be decided according to the operating speed and the load conditions. This paper proposes speed control of IPMSM using ALM-FNN, current control of model reference adaptive fuzzy control(MTC) and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled HIC, the operating characteristics controlled by efficiency optimization control are examined in detail.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2763-2765
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• 2003

Generally, we made use of PID control for torque control, speed control and stability, Hence, dynamic characteristic of DC motor has been studied for stable drive and accurate speed control by many engineers. But, in this paper, we applied genetic algorithm to current control for robust control and stability In conclusion, we prove that current control of genetic algorithm can be high efficiency.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권6호
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• pp.402-407
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• 2004

This paper proposed a new speed sensorless control method for AC servo motor, which is based on the current observer. Recently, the study in the estimation using a observer is being widely progressed. In order to prove the superiority of the current observer, we made a comparison between the simulation using current observer and that using Luenberger observer. And also, a comparative experiment of the speed control of the AC servo motor using a sensor and using current observer has been executed. The experiment result shows that the estimation using current observer is superior to the estimation using Luenberger observer or to the speed response using a sensor.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 하계학술대회 논문집 A
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• pp.283-286
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• 1994

A speed control system of the induction motor drives by the load commutated current source inverter is studied in this paper. In general, since the rotating equipments such as pumps, fans and blowers do not require necessarily the accurate speed control performance, a voltage control and a speed sensorless vector control systems are mainly used for this application. In this paper, a new control scheme which has better performance than the voltage control system and simpler control structures than the vector control system is presented. This new proposed method controls the angle of the current vector using the relationship between the inverter output voltage and current, as well as uses the simple method to compensate the effect of the output capacitors.

• Journal of Power Electronics
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• 제5권1호
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• pp.11-19
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• 2005

This paper describes the two control techniques to perform the sensorless vector control of a PMSM by injecting the high frequency voltage to the stator terminal. The first technique is the estimation algorithm of the initial rotor position. A PMSM possesses the saliency which produces the ellipse of the stator current when the high frequency voltage is injected into the motor terminal. The major axis angle of the current ellipse gives the rotor position information at a standstill. The second control technique is a sensorless control algorithm that injects the high frequency voltage to the stator terminal in order to estimate the rotor position and speed. The rotor position and speed for sensorless vector control is calculated by appropriate signal processing to extract the position information from the stator current at low speeds or standstill. The proposed sensorless algorithm using the double-band hysteresis controller exhibits excellent reference tracking and increased robustness. Experimental results are presented to verify the feasibility of the proposed control schemes. Speed, position estimation and vector control were carried out on the floating point processor TMS320VC33.

• Journal of Power Electronics
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• 제12권3호
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• pp.468-476
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• 2012

The interior permanent magnet synchronous motor (IPMSM) has been widely used in electric vehicle applications due to its excellent power to weigh ratio. This paper proposes the maximum torque control of an IPMSM drive using an adaptive learning (AL) fuzzy neural network (FNN) and an artificial neural network (ANN). This control method is applicable over the entire speed range while taking into consideration the limits of the inverter's rated current and voltage. This maximum torque control is an executed control through an optimal d-axis current that is calculated according to the operating conditions. This paper proposes a novel technique for the high performance speed control of an IPMSM using AL-FNN and ANN. The AL-FNN is a control algorithm that is a combination of adaptive control and a FNN. This control algorithm has a powerful numerical processing capability and a high adaptability. In addition, this paper proposes the speed control of an IPMSM using an AL-FNN, the estimation of speed using an ANN and a maximum torque control using the optimal d-axis current according to the operating conditions. The proposed control algorithm is applied to an IPMSM drive system. This paper demonstrates the validity of the proposed algorithms through result analysis based on experiments under various operating conditions.

• 수산해양기술연구
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• 제56권1호
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• pp.71-81
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• 2020

This paper proposes a possibility of using active front-end rectifier with the SVPWM method for induction motor speed control, which is applicable to small electric propulsion boats. The proposed method can produce a more precise sinusoidal input current waveform and a higher power factor than conventional methods. Its speed, torque, input current, DC voltage, and load current control performance are similar to or better than those of conventional methods. Through computer simulations using the PSIM program, the validity of the proposed method was verified by comparing and analyzing the characteristics of the conventional methods and the proposed method.