• Proceedings of the KIPE Conference
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• 2012.07a
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• pp.75-76
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• 2012

본 논문에서는 고속 운전을 위하여 IPMSM(Interior Permanent Magnet Synchronous Motor)의 약계자 운전을 제시한다. IPMSM 운전을 위한 약계자 제어기는 SMO(Sliding Mode Observer)를 통한 추정 속도와 실제 전동기의 속도를 비교하여 나타나는 오차값을 피드백 받아서 이용한다. 기본적으로 속도 오차가 없는 상황에서는 MTPA(Maximum Torque Per Amp.)에서 동작 시키고 속도 오차가 증가하면 이 오차에 대해 d축 전류를 음의 방향으로 증가시켜 약계자 제어를 한다. 본 논문에서는 제시한 약계자 운전으로 IPMSM에 적용시험을 한다. 그리고 시험결과를 분석하여 본 논문의 타당성을 입증한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.2
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• pp.349-356
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• 2018

In this study, the synchronous control system is designed to restrain the speed difference generated between two propellers, namely, synchronous error in a dual electric propulsion system of unmanned surface vehicle, fish finder boat, etc. The control system based on coupling structure is composed of pre-filters and speed controllers for each propulsion system and a synchronous controller cross-coupled between two propulsion systems. The pre-filter and speed controller are designed in order that the propulsion system may follow the speed reference without overshoot and input saturation. And the synchronous controller is designed in consideration of damping and quickness of the synchronous controller system after analyzing effect of the skew disturbance and mismatched dynamic characteristics on synchronous error. Finally, the simulation results show that the designed control system is effective for elimination of synchronous error.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.3
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• pp.53-61
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• 2006

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy nile as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive fuzzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.5
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• pp.413-419
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• 2004

In the induction motor direct vector control system using the Gopinath model flux observer, the deterioration of the dynamic response due to the detuned rotor time constant is investigated. To solve this problem, the on line estimation algorithm of the rotor time constant using model reference adaptive control is proposed. The effect of the motor parameter variation on the rotor time constant estimation is analyzed through experiment. The estimation error due to the parameter variation converges within 5%. Thus applying the proposed algorithm to the Gopinath model flux observer, the robust direct vector control system of the induction motor to the parameter variation can be implemented.

• Journal of the Korean Society of Propulsion Engineers
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• v.22 no.2
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• pp.131-137
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• 2018

This paper analyzes the probability of failure for the equivalence ratio error. The control error of the equivalence ratio is affected by the aleatory and epistemic uncertainties. In general, reliability analysis techniques are easily incorporated to handle the aleatory uncertainty. However, the epistemic uncertainty requires a new approach, as it does not provide an uncertainty distribution. The Bayesian inference incorporates the reliability analysis results to handle both uncertainties. The result gives a distribution of failure probability, whose equivalence ratio does not meet the requirement. This technique can be useful in the analysis of most engineering systems, where the aleatory and epistemic uncertainties exist simultaneously.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.5
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• pp.413-418
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• 2002

This paper proposed a feed-forward adaptive position controller that is robust for variable Inertia. The control system consists of PI Position controller, feed-forward and model reference adaptive control. A parameter g(t) of the feed-forward adaptive position controller is adapted by using both the reference model speed and position error. So it improves the transient response and reduces the settling time. And normalization function Is used to make linear adaptation time. The validity of the feed-forward adaptive controller is confirmed by simulation results.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.5
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• pp.56-61
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• 2000

Robust control for DC motor is needed according to the highest precision of industrial automation. However, when a motor control system with PID controller has an effect of load disturbance, it is very difficult to guarantee the robustness of control systems.In this paper, PID-Expert hybrid control method for motor control system as a compensation method solving this problem is presented. If PID control system is stable, the Expert controller is idle. if the error hits the boundary of the constraint, the Expert controller begins operation to force the error back to the constraint set. The disturbance effect decrease remarkably, robust speed control of DC motor using PID-Expert Hybrid controller is demonstrated by the simulation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.3
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• pp.103-108
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• 2009

Robust control for DC motor is needed according to the highest precision of industrial automation. However, when a motor control system with PID controller has an effect of load disturbance, it is very difficult to guarantee the robustness of control system. In this paper, PID-Expert hybrid control method for motor control system as a compensation method solving this problem is presented. If PID control system is stable, the Expert controller is idle. if the error hits the boundary of the constraint, the Expert controller begins operation to force the error back to the constraint set. The disturbance effect decrease remarkably, robust speed control of DC motor using PID-Expert Hybrid controller is demonstrated by the simulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.8
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• pp.1993-1997
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• 2009

Robust control for DC servo motor is needed according to the highest precision of industrial automation. However, when a motor control system with PID controller has an effect of load disturbance, it is very difficult to guarantee the robustness of control system. As a compensation method solving this problem, in this paper, PID-neural network hybrid control method for motor control system is presented. The output of neural network controller is determined by error and rate of error change occurring in load disturbance. The robust control of DC servo motor using neural network controller is demonstrated by computer simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.673-676
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• 2005

In this paper, we propose a improved control performance for autonomous mobile robot using the embeded system. The proposed PID(PPID) control method removes steady state error, and has good performance in transient response. The integral term in the proposed method accumulates error in increment section, inverses errors in decrement section, and increases the effect of integral coefficient around target. Therefore, the PPID control method decreases the overshoot, and makes settling time faster than general PID(GPID) control method. In order to verify the effectiveness of the proposed method(PPID), we simulated the position control of DC motor, and experimented the velocity control of autonomous mobile robot. The PPID control method demonstrated good control performance that decrease overshoot and settling time in simulation and experiments.