• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.70-70
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• 2000

Recently, a neuro-fuzzy approach, a combination of neural networks and fuzzy reasoning, has been playing an important role in the motor control. In this paper, a novel method of fiction compensation using neuro-fuzzy architecture has been shown to significantly improve the performance of a DC motor system with nonlinear friction characteristics. The structure of the controller is the neuro-fuzzy network with the TS(Takagi-Sugeno) model. A back-propagation neural network based on a gradient descent algorithm is employed, and all of its parameters can be on-line trained. The performance of the proposed controller is compared with both a conventional neuro-controller and a PI controller.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3A
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• pp.311-317
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• 2004

A low loss reflection type analog phase shifter which is realized with two varactors and branch coupler. is described. A new design technique for the large phase shift is applied using short stub microstripe lines. This paper presented the realization of design and experimental results. Through the experiment using the duroid microstrip line and varactors, the phase of 2.26GHz signal is controlled to the DC voltage with the phase shift range of 170$^{\circ}$. The achieved insertion loss of less than 3㏈ and the return loss of mote than 12㏈ are achieved over all phase states.