• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.514-518
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• 2005

In this paper an adaptive fuzzy PI controller with feedback linearizing meth od is implemented to controlling flux and torque separately in induction motor. In this paper first decoupling of torque and flux which are outputs to be controlled, is achieved by using feedback linearization methodology. Then for reducing the effect of noise and rejection of disturbance, main part of controller which is adaptive PI fuzzy controller, is designed. Coefficients of PI controller are determined by defined fuzzy rules due to error dynamic. Inputs of fuzzy system are defined sliding surfaces which consist of torque and flux errors. The main contribution of this paper is effect reduction of noise and disturbance on torque and flux which is based on fuzzy logic and nonlinear control. At last the effectiveness of the proposed control scheme in presence of noise and load disturbance is simulated and comprised to applying sliding method. The results verify better effectiveness of the proposed method for effect reduction of noise and disturbance.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1289-1301
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• 2011

In this paper, flux estimation method at the Induction motor is applied to stability flux estimate of possibility in overall speed domain. angle operation has voltage and current and speed information using the Induction motor direct control method. Induction motor direct control is material to flux information. Exact flux estimation method to using current model flux estimator of low-speed domain and voltage model flux estimator of high-speed domain. Speed and current and flux controller using PI controller. And error of integral requital for add to Anti-Windup PI controller. Verified to performance of Current model Flux controller and voltage model flux controller using Matlab / Simulink. Analysis has parameter influence of direct vector control and indirect vector control at the Induction motor vector control. So, verified to minute control. Analyzed to simulation result and proof to validity of presented algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5416-5426
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• 2012

This study uses the algorithm which estimates the magnetic flux using different models in the low speed driving area and the high speed driving area by the voltage-current model synthesis magnetic flux Estimator and, from this result, estimates the magnetic flux angle to achieve the stable speed control through all the areas from the low speed to the high speed drive. In particular, the current change and the magnetic flux change under variable load were estimated in real time in the low speed area and this made the control characteristic improved in the low speed area. According to this, even under variable load, the more stable simulation and experiment could have been completed using PI current controller and PI flux controller in all the areas. As a result, the outstanding speed control characteristic has been achieved.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.11
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• pp.664-670
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• 2004

This paper presents a new velocity estimation strategy of a non-salient permanent magnet synchronous motor (PMSM) drive without high frequency signal injection or special PWM pattern. This approach is based on the d-axis current regulator output voltage of the drive system that has the information of rotor position error. The rotor velocity can be estimated through a rotor position tracking PI controller that controls the position error to zero. For zero and low speed operation, PI controller gains of rotor position tracking controller have a variable structure according to the estimated rotor velocity. In order to boost the bandwidth of PI controller around zero speed, a loop recovery technique is applied to the control system. The proposed method only requires the flux linkage of permanent magnet and is insensitive to the parameter estimation error and variation. The designers can easily determine the possible operating range with a desired bandwidth and perform the vector control even at low speeds. The experimental results show the satisfactory operation of the proposed sensorless algorithm under rated load conditions.

• Journal of Power Electronics
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• v.12 no.2
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• pp.305-316
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• 2012

The paper presents an accurate loss model based controller of an induction motor to calculate the optimal air gap flux. The model includes copper losses, iron losses, harmonic losses, friction and windage losses, and stray losses. These losses are represented as a function of the air gap flux. By using the calculated optimal air gap flux compared with rated flux for speed sensorless indirect vector controlled induction motor, an improvement in motor efficiency is achieved. The motor speed performance is improved using a fuzzy logic speed controller instead of a PI controller. The fuzzy logic speed controller was simulated using the fuzzy control interface block of MATLAB/SIMULINK program. The control algorithm is experimentally tested within a PC under RTAI-Linux. The simulation and experimental results show the improvement in motor efficiency and speed performance.

• Proceedings of the Korean Society of Laser Processing Conference
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• 2006.06a
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• pp.129-134
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• 2006

In this paper, actuator based on voice coil motor is designed and estimated to check the capability whether that system is in design specification or not. Design specification is moving range as $20{\times}20mm^2$ a along XY-axis in plane and maximizes bandwidth for high speed. The type of voice coil motor is selected with regarding design specification and minimizes the size of actuator. Flux density of designed voice coil motor is simulated. And PI controller is designed to maximize bandwidth of actuator. Finally, characteristic of designed actuator is simulated and that result reveals the validity of presented design.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1086-1087
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• 2007

This paper presents a implementation of speed sensorless vector control algorithm of induction motor using MATLAB/SIMULINK. The proposed method utilize the combination of the voltage model based on stator equivalent model and the current model based on rotor equivalent model, which enables stable estimation of rotor flux. Estimated rotor speed, which is used to speed controller of induction motor, is based on estimated flux. The overall system consisted of speed controller with the most general PI controller, current controller, flux controller. Speed sensorless vector control algorithm is implemeted as block diagrams using MATLAB/SIMULINK. Realtime control is perform by dSPACE DS1104 control board and Real-Time-Interface(RTI).

• Journal of the Korean Society of Safety
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• v.14 no.4
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• pp.108-113
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• 1999

This paper presents a study on fuzzy speed and flux controller used in a vector control of a CRPWM(Current Ragulated PWM) induction motor drive. In this paper, an approach for an easier design of the fuzzy controller is presented in order to obtain the desired value for the response time with minimal overshoot and to improve the steady state performance for speed step commands. The fuzzy controller is constructed only upon the knowledge of the motor behaviour and the desired speed response, and provides fast and robust control by reducing the effects of nonlinearities, parameter changes and load disturbance. The results of applying the fuzzy logic controller to an IM drive system are compared with those obtained by application of a conventional PI controller. The fuzzy controller provided a better response than the PI controller.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1111-1113
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• 2003

This paper proposes an effective self-turning algorithm based on Artificial Neural Network (ANN) for fuzzy speed control of the indirect vector controlled induction motor. Indirect vector control method divides and controls stator current by the flux and the torque producing current so that the dynamic characteristic of induction motor may be superior. However, if motor parameter changes, the flux current and the torque producing one's coupling happens and deteriorates the dynamic characteristic. The fuzzy speed controller of an induction motor has the robustness over the effect of this parameter variation than a conventional PI speed controller in some degree. This paper improves its adaptability by adding the self-tuning mechanism to the fuzzy controller. For tracking the speed command, its membership functions are adjusted using ANN adaptation mechanism. This adaptability could be embodied by moving the center positions of the membership functions. Proposed self-tuning method has wide adaptability than existent fuzzy controller or PI controller and is proved robust about parameter variation through Matlab/Simulink simulation.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.77-78
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• 2008

This paper presents to control speed of induction motors with uncertainties. We use an adaptive backstepping controller with fuzzy neural networks(FNNs) and model reference adaptive system(MRAS) at Indirect vector control method. The adaptive backstepping controller using FNNs can control speed of induction motors even we have a minimum of information. And this controller can be used to approximate most of uncertainties which are derived from unknown motor parameters, load torque such as disturbances. MRAS estimates to rotor resistance and also can find optimal flux to minimize power losses of Induction motor. Indirect vector PI current controller is used to keep rotor flux constant without measuring or estimating the rotor flux. Simulation and experiment results are verified the effectiveness of this proposed approach.