• 대한전기학회논문지:시스템및제어부문D
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• 제50권3호
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• pp.107-113
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• 2001

This paper deals with speed control of DC servo motor using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm. Conventionally a PID controller has been used in the industrial control. But a PID controller should produce suitable parameters for each system. Also, variables of the PID controller should be changed according to environments, disturbances and loads. In this paper described by a experiment that contained a method using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm, we developed speed characteristics of a DC servo motor on variable loads. The parameters of the controller are determined by neural network performed on on-line system after training the neural network on off-line system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2255-2257
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• 2002

In this paper, Fuzzy-Observer is designed to control the speed of DC Servo Motor. Fuzzy-Observer estimates the speed of DC Servo Motor with the measurement of the amature current. The constant speed and variable speed experiments are excuted to evaluate the performance of Fuzzy-Observer.

• 한국조명전기설비학회지:조명전기설비
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• 제11권1호
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• pp.81-91
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• 1997

본 논문에서는 시스템 모델링이 어렵과 복잡한 자동화 설비를 위한 간단한 지능제어방식을 제안하나. 제안된 방식은 시스템 모델링이 불확실한 시스템에 대하여 입력신호와 직접관계되지 않은 비선형 함수의 동정은 퍼지-신경회로망을 이용하고, 입력신호와 관계되는 비선형 함수는 동정을 하지 않고 임의의 양의 실수로 놓으므로 기존의 전체함수 동정보다 적은량으로 동정할 수 있고, 동정된 정보를 이용하여 비선형 제어기를 설계하는 간단한 제어방식이다. 제안된 제어 방식의 유용성을 확인하기 위하여 자동화 설비에 액츄에이터로 많이 사용되는 직류 서보전동기를 이용한 역진자 시스템에 적용하여 시뮬레이션 및 실험을 하고, 제안된 제어방식을 기존의 신경회로망 제어방식과 제어성능을 비교 검토한다.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권12호
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• pp.663-670
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• 2003

This paper deals with speed control of DC servo motor using a Adaptive high gain obserber. In this parer, the gain of the observer is properly set up using the fuzzy control and adaptive high gain observer that have a superior transient characteristic and is easy to implement compared the existing method is designed. In order to verify the performance of the Adaptive high gain observer which is proposed in this paper, it is compared estimate performance of High-gain Observer and Adaptive High Gain Observer with the computer simulation. Effectiveness of the proposed high gain observer is proved from the experiment to compare the case with a speed sensor to the case with Adaptive high gain observer in the speed control of DC servo motor.

• Journal of Advanced Marine Engineering and Technology
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• 제31권4호
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• pp.441-447
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• 2007

The speed and torque control performance of the DC motor used in the industrial field is affected by the change of parameters. In particular the change of armature resistance can be greatly originated from various load conditions. Accordingly a large number of studies to estimate those problems in the servo motor control field have already been under way. In this paper, the armature resistance on the DC motor under several load and speed conditions is estimated by the Artificial Neural Network(ANN), and the validity is proven by the speed estimation of sensorless speed control system.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권5호
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• pp.338-344
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• 2004

This paper presents a sensorless speed control of a DC servo motor using a fuzzy-sliding observer in the presences of load disturbances. A fuzzy-sliding observer is proposed in order to estimate the speed of a motor rotor. First, a sliding observer is used to estimate the derivative of the armature current directly using the armature current mesured in the DC servo motor. Second, the optimal gain of the Luenberger observer is set up using the fuzzy control. Experimental results show the good performance in the DC servo motor system with the proposed fuzzy-sliding observer.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1113-1115
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• 1996

The servo system requires faster and more accurate dynamic responses. A new technique for the position control of DC servo motors is presented in this paper. The proposed technique employs a Self Tuning Regulator Proportional Integral Derivative(STR PID) position control systems in order to improve the dynamic performance of a DC servo motor. Recursive -least -squares (RLS) method is used in order to estimate the STR PID coefficients, $K_P$, $K_I$, and $K_D$. In order to consider dynamics such as voltage, angular velocity, and rotor angle, the above method was applied position control system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2167-2169
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• 2003

The LQG regulator presented in this dissertation is designed to improve system stability with the structure in which Kalman Filters and LQ regulator are inter-connected to avoid disturbances on the system. For the sake of performance evaluation of the LQG regulator in this dissertation, the regulator is put in use for the speed control of DC Servo Motors without speed sensors on them to realize speed control without sensors by estimating uncertain speed with its state signals and to construct an optimal controller using the LQG regulator. Furthermore, state estimation process with white noise entered onto the systems is been experimented through a series of simulation. As a consequence, the process of estimating speed signals out of the signals with white noise inputs is confirmed using Kalman Filter.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.241-243
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• 1993

In this paper, a class of neural-network controllers with two inputs of error and error change, is applied to the position control of D.C. servo system. The proposed controller is learned by error back-propagating error information to compensate the weighting value using its previous derivatives and to decrease exponentially a series of self learning coefficients. Through the simulations and implementations, the effectiveness and superiority to the conventional fuzzy controller is proved.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.244-247
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• 1993

In this paper, Fuzzy-Neural Network Control system that has the characteristic of fuzzy control to be controlled easily end the good characteristic of a artificial neural network to control the plant due to its learning is presented. A fuzzy rule to be applied is selected automatically by the allocated neurons. The neurons correspond to Fuzzy rules which ere created by a expert. To adaptivity, the more precise modeling is implemented by error beck-propagation learning of adjusting the link-weight of fuzzy membership function in Fuzzy-Neural Network. The more classified fuzzy rule is used to include the property of Dual Mode Method. To test the effectiveness of the algorithm presented above, the simulation for position end velocity of DC servo motor is implemented.