• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.291-298
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• 2017

In recent years, the increase of secondary controls within vehicles requires a mechanism to integrate various controls into a single device. This paper presents control performance of an integrated magnetorheological (MR) haptic device which can adjust various in-vehicle comfort instruments. As a first step, the MR fluid-based haptic device capable of both rotary and push motions within a single device is devised as an integrated multi-functional instrument control device. Under consideration of the torque and force model of the proposed device, a magnetic circuit is designed. The proposed MR haptic device is then manufactured and its field-dependent torque and force are experimentally evaluated. Furthermore, an inverse model compensator is synthesized under basis of the Bingham model of the MR fluid and torque/force model of the device. Subsequently, haptic force-feedback maps considering in-vehicle comfort functions are constructed and interacts with the compensator to achieve a desired force-feedback. Control performances such as reflection force are experimentally evaluated for two specific comfort functions.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.54-60
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• 2002

In this paper, we proposed a hybrid neural network-fuzzy controller which compensate a output of neural network controller. Even if learn by neural network controller, it can occur an bad results from disturbance or load variations. So in order to adjust above case, we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of loaming a inverse model neural network of Plant, so a expected dynamic characteristics of plant can be got. As the results of simulation through the second order plant, we confirmed that the proposed speed controller get a good response compare with a neural network controller. We implemented the controller using the DSP processor and applied in a hydraulic servo system. And then we observed an experimental results.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.233-237
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• 2012

In this paper, We propose a control system which compensate a output of a main Neual Network using a RTDNN(Recurrent Time Delayed Neural Network) with a FLC(Fuzzy Logic Controller)After a learn of main neural network, it can occur a Over shoot or Under shoot from a disturbance or a load variations. In order to adjust above case, we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of learning a inverse model neural network of plant, so a expected dynamic characteristics of plant can be got. We can confirm good response characteristics of proposed neural network controller by the results of simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.334-341
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• 2003

In this paper, we proposed a recurrent time delayed neural network(RTDNN) controller which compensate a output of neural network controller. Even if learn by neural network controller, it can occur an bad results from disturbance or load variations. So in order to adjust above case, we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of learning a inverse model neural network of plant, so a expected dynamic characteristics of plant can be got. As the results of simulation through the second order plant, we confirmed that the proposed recurrent time delayed neural network controller get a good response compare with a time delayed neural network(TDU) controller. We implemented the controller using the DSP processor and applied in a hydraulic servo system. And then we observed an experimental results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2C
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• pp.191-199
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• 2006

In this paper, we propose a method to implement a predistorter of the $rho$-th order inverse filter structure to prevent signal distortion and spectral re-growth due to the high PAPR (peak-to-average ratio) of the OFDM signals and the non-linearity of high-power amplifiers. We model the memory-less non-linearity of the high-power amplifier with a polynomial model and utilize the inverse of the model, the $rho$-th order inverse filter, for the predistorter. Once the non-linearity is modeled with a polynomial, since we can determine the $rho$-th order inverse filter only with the coefficients of the polynomial, large memory is not required. To update the coefficients of the non-linear high-power amplifier model, we can use LMS or RLS algorithms. The convergence speed is high since the number of coefficients is small, and the computation is simple since manipulation of complex numbers is not necessary.