• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.142-144
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• 1993

In this study, a Self-learning Neural-Fuzzy Networks is presented, Because of the fuzzy controller property, the designing problems of fuzzy if-then rules, membership functions and inference methods are very complex task. Thus in this paper we proposed the Neural-Fuzzy Networks composed by Sugeno and Takagi's fuzzy inference method and learned by using temporal back propagation algorithm. The proposed method can refine automatically the fuzzy if-then rules without human expert's knowledges. The induction motor servo system is used to demonstrate the effectiveness of the proposed control scheme and the feasibility of the acquired fuzzy controller. All results are supported by simulation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.9
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• pp.646-654
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• 1988

In this paper, a speed controller using a microcomputer is implemented and applied to a DC Servo Motor. Adaptive control is applied to a system for which a priori knowledge to its mathematical model is insufficient, on the basis of input and output data an apropriate controller is constructed through which the system input is synthesized. The pole-placement PID self tuning control algorithms as a control algorithm is used to compare the performance of the controller with that of the classical PID controller through computer simulations and experiments.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.207-210
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• 1998

Brushless servomotor systems offer a great advantae in unmanned factories where a great number of servomotors are employed, because of its easy maintenance charateristics and controllability. This paper propose a sinusoidal type brushless DC motor speed servo system which has high efficiency and usability in industrial field as described above. And this servo system is realized by a new Auto-Tuning PI control algorithm and verified it's practical potential of implementation by self-designed driver system. In particular, DSP(digital signal processor) is adopted in this study as controller and sensor signal processor owing to their fast computational capability and suitable architecture.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.870-872
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• 1993

With the using the microprocessor, this paper presents DC servo motor control characteristics by Self-Tuning PID controller and considers position control response with controller of DC servo motor for robot drive. As this system is supported by a channel, it is considered to enough application effect in industry region such as needing multi joint robot and precision parallel driving.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.7
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• pp.103-108
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• 2007

Neural network is used in many fields of control systems currently. However, It is not easy to obtain input-output pattern when neural network is used for the system of a single feedback controller and it is difficult to get satisfied performance with neural network when load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object in place of activation function of Neural Network output node. As the Neural Network self adaptive control system is designed in simple structure neural network input-output pattern problem is solved naturally and real tin Loaming becomes possible through general back propagation algorithm. The effect of the proposed Neural Network self adaptive control algorithm was verified in a test of controlling the speed of a A.C. servo motor equipped with a high speed computing capable DSP (TMS320C32) on which the proposed algorithm was loaded.

• Journal of Power Electronics
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• v.15 no.5
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• pp.1409-1420
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• 2015

In this paper, an algorithm using two bi-quad filters to suppress the wide-band resonance for PMSM servo systems is proposed. This algorithm is based on the double bi-quad filters structure, so it is named, "double bi-quad filter." The conventional single bi-quad filter method cannot suppress unexpected mechanical terms, which may lead to oscillations on the load side. A double bi-quad filter structure, which can cancel the effects of compliant coupling and suppress wide-band resonance, is realized by inserting a virtual filter after the motor speed output. In practical implementation, the proposed control structure is composed of two bi-quad filters on both the forward and feedback paths of the speed control loop. Both of them collectively complete the wide-band resonance suppression, and the filter on the feedback path can solve the oscillation on the load side. Meanwhile, with this approach, in certain cases, the servo system can be more robust than with the single bi-quad filter method. A step by step design procedure is provided for the proposed algorithm. Finally, its advantages are verified by theoretical analysis and experimental results.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.246-249
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• 1988

In this paper, a new design technique which uses weighted least-sqare approach for the solution of the pole assignment problem is represented. This technique maybe used to assign some closed loop poles to places which reduce the large system input and output variance due to near pole-zero condition. The least-square approach is also applied to the design of servo self-tuning controller with integrator.

• International Journal of Precision Engineering and Manufacturing
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• v.2 no.2
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• pp.11-16
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• 2001

This paper presents an in self-calibration method to corrent the Z-directional distortion of AFM(Atomic Force Microscopy).

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.49-54
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• 2003

In this paper, we propose a controller with the self-organizing neural network compensator for compensating PID controller's response. PID controller has simple design method but needs a lot of trials and errors to determine coefficients. A neural network control method does not have optimal structure as the parameters are pre-specified by designers. In this paper, to solve this problem, we use a self-organizing neural network which has Back Propagation Network algorithm using a Gaussian Potential Function as an activation function of hidden layer nodes for compensating PID controller's output. Self-Organizing Neural Network's learning is proceeded by Gaussian Function's Mean, Variance and number which are automatically adjusted. As the results of simulation through the second order plant, we confirmed that the proposed controller get a good response compare with a PID controller. And we implemented the of controller performance hydraulic servo motor system using the DSP processor. Then we observed an experimental results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.11
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• pp.2189-2198
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• 1994

In this paper, a self-tuning fuzzy logig controller is implemented to control a DC servo motor by the self-tuning technique based on fuzzy meta-rules with learning in several algorithms to improve the performance of the fuzzy logic controller used in a fuzzy control system. Simulations and experimental results of the self-tuning fuzzy logic controller are compared with those of the fuzzy logic controller to evaluate its performance.